We develop the theory of Heyting locally small spaces, including Stone-like dualities such as a new version of Esakia duality and a system of concrete isomorphisms and equivalences. In such a way, we continue building tame topology, realising Grothendieck's ideas. We use up-spectral spaces and define the standard up-spectralification of a Kolmogorov locally small space. This research gives more understanding of locally definable spaces over structures with topologies.

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.

Containing hundreds of spectral bands (features), hyperspectral images (HSIs) have high ability in discrimination of land cover classes. Traditional HSIs data processing methods consider the same importance for all bands in the original feature space (OFS), while different spectral bands play different roles in identification of samples of different classes. In order to explore the relative importance of each feature, we learn a weighting matrix and obtain the relative weighted feature space (RWFS) as an enriched feature space for HSIs data analysis in this paper. To overcome the difficulty of limited labeled samples which is common case in HSIs data analysis, we extend our method to semisupervised framework. To transfer available knowledge to unlabeled samples, we employ graph based clustering where low rank representation (LRR) is used to define the similarity function for graph. After construction the RWFS, any arbitrary dimension reduction method and classification algorithm can be employed in RWFS. The experimental results on two well-known HSIs data set show that some dimension reduction algorithms have better performance in the new weighted feature space.

We proposed in this work the introduction of a new vision of stochastic processes through geometry induced by dilation. The dilation matrices of a given process are obtained by a composition of rotation matrices built in with respect to partial correlation coefficients. Particularly interesting is the fact that the obtention of dilation matrices is regardless of the stationarity of the underlying process. When the process is stationary, only one dilation matrix is obtained and it corresponds therefore to Naimark dilation. When the process is nonstationary, a set of dilation matrices is obtained. They correspond to Kolmogorov decomposition. In this work, the nonstationary class of periodically correlated processes was of interest. The underlying periodicity of correlation coefficients is then transmitted to the set of dilation matrices. Because this set lives on the Lie group of rotation matrices, we can see them as points of a closed curve on the Lie group. Geometrical aspects can then be investigated through the shape of the obtained curves, and to give a complete insight into the space of curves, a metric and the derived geodesic equations are provided. The general results are adapted to the more specific case where the base manifold is the Lie group of rotation matrices, and because the metric in the space of curve naturally extends to the space of shapes, this enables a comparison between curves’ shapes and allows then the classification of processes’ measure.

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.

In this study, a new method for synthesizing Ag-NaYF4:Yb3+/Er3+ @ SiO2 nanocomposites was introduced. Using a hydrothermal method, the synthesized Yb3+- and Er3+-codoped NaYF4 upconversion luminescent materials and Ag nanoparticles were doped into upconversion nanomaterials and coated with SiO2 up-conversion nanomaterials. This material is known as Ag-UCNPs-SiO2’ it improves both the luminous intensity because of the doped Ag nanoparticles and has low cytotoxicity because of the SiO2 coating. The morphology of UCNPs was observed using scanning electron microscopy (SEM), and the mapping confirmed the successful doping of Ag nanoparticles. Successful coating of SiO2 was confirmed using transmission electron microscopy (TEM). Fluorescence spectra were used to compare changes in luminescence intensity before and after doping Ag nanoparticles. The reason for the increase in luminescence intensity after doping with Ag nanoparticles was simulated using first-principles calculations. The cytotoxicity of Ag-UCNPs-SiO2 was tested via the cell counting kit-8 (CCK-8) method, and its imaging ability was characterized using the micro-CT method.

Efficient commencement of clinical trials is pivotal for their successful execution. The purpose of this study was to determine and examine the factors that contribute to trial start-up delays. The study assessed 20 clinical trials' start-up stages in order to identify relevant variables. Results showed that trials with an emphasis on devices, trials that did not outsource, trials that utilized less auxiliary services, and trials that employed interventional designs had quicker start-up times than observational studies. Furthermore, approval times were substantially shortened by using a centralized Institutional Review Board (IRB) rather than a local IRB. Furthermore, compared to studies that successfully enrolled subjects, those who failed to do so required more time to complete contracts and budgets and secure IRB clearance. This study underscores the diverse impact of clinical trial features on start-up timelines. Understanding these factors is crucial for optimizing the initiation process of clinical trials.

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.

Wildfires present a significant risk to societies globally due to population growth, con- centrated activities, climate change, and extreme environmental conditions. To establish effective fire suppression and management policies, it is crucial to determine whether the distance between ignition points and urban areas is increasing or decreasing. This study analyses 101,597 fires in Spain and California between 2007 and 2015, using the Mann-Kendall test to determine trends. The study analyses the location and cause of fires to determine their distance from human constructions over time at two spatial scales. The findings suggest that wildfires are not moving away from buildings and, in some areas, are moving significantly closer.

Globally, karst aquifers store large amount of precious water and create beautiful karst springs in many places. However, most of the karst springs flow declined, and some of the karst springs dried up with the effects of extensive groundwater development and climate variation. In order to obtain better understanding of factors contributing to the drying-up of karst springs, this study introduced grey system models to quantify spring flow taking Jinci Springs (China) as an example, which dried up in May 1994. Based on the characteristics of Jinci Springs flow, the spring flow was divided into two stages: the first stage (1954-1960), when the spring flow was affected only by climate variation; and the second stage (1961-1994), when the flow was impacted by both climate variation and anthropogenic activities. Results showed that the Jinci Springs flow had strong relations with precipitation occurring one year and three years earlier in the first stage. Subsequently, a grey system GM (1, 3) model with one-year and three-year lags was set up for the first stage. By using the GM (1, 3) model, we simulated the spring flow in the second stage under effects of climate variation only. Subtracting the observed spring flow from the simulated flow, we obtained the contribution of anthropogenic activities to Jinci Springs cessation. The contribution of anthropogenic activities and climate variation to Jinci Springs cessation was 1.46 m³/s and 0.62 m³/s, respectively. Finally, each human activity causing spring flow decline was estimated. The methods are useful to describe the karst hydrological processes under the effects of anthropogenic activities and climate variation.

Stand Up Paddleboard (SUP) surfing entails riding breaking waves and maneuvering the board on the wave face in a similar manner to traditional surfing. Despite some scientific investigations on SUP, little is known about SUP surfing. The aim of this study was to investigate the physiological response during SUP surfing sessions and to determine how various environmental conditions can influence this response. Heart rate (HR) of an experienced male SUP surfer aged 43 was recorded for 14.9 hours during ten surfing session and synced with on board video footage to enable the examination of the effect of different surfing modes and weather conditions on exercise intensity. Results indicated the SUP surfer's HR was above 70% of HR_max during 85% of each session, with the greatest heart rates were found during falls off the board (~85%HR_max) and while paddling back to the peak (~83%HR_max). Total time surfing a wave was less than 5% with the majority of time spend paddling back into position. Wind speed positively correlated with HR (r² = 0.062, p = 0.012) and wave height negatively correlated wave caching frequency (r² = 0.54, p = 0.025). The results highlight the aerobic fitness for SUP surfing, that wave riding, paddling back to the peak and falls appear to be associated with the greatest cardiovascular demand and that environmental conditions can have an effect of physiological response during SUP surfing sessions.

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.

. Background/Objectives: Crohn's disease is a chronic and debilitating intestinal disorder that alternates between remission and active flare-ups, often leading to hospitalization. Social support is known to enhance adaptation to the disease and modulate stress perception in patients, while stress may exacerbate symptoms. The aim of this study was to examine the roles of perceived stress and social support in Crohn's disease and their impact on the frequency of flare-ups. Methods: A cross-sectional observational study was conducted, assessing stress and social support in a cohort of 91 patients with Crohn's disease during flare-up and remission phases. The Perceived Stress Scale (PSS-14) and a Social Support Questionnaire were utilized for evaluation. We examined the relationship between stress and social support in Crohn's disease. The interaction between the variables studied was also studied, considering the stage of the disease. Finally, we realized analysis of the influence of these two variables on the development of flares in Crohn's disease. Results: The study revealed that patients experience higher stress levels during flare-ups and that these levels are amplified by a lack of social support. A significant relationship was identified between the levels of social support and the occurrence of flare-ups, indicating that better social support is associated with fewer flare-ups.. Conclusions: Patients with Crohn's disease in the flare-up phase are subject to considerable stress. Deficit in social support is linked to an increase in stress levels. The interplay of social support and stress plays a critical role in the development of flare-ups.

Up-conversion luminescence (UCL) materials are of great importance due to their unique optical properties. In particular, the red UCL, falling into the optical transmittance window of biological tissues, can realize deep tissue penetration depth and high-resolution bioimaging. In this work, effective red UCL is achieved in the ternary sulfide by the introduction of Tm3+ or Ho3+ ions into Er3+ doped NaYS2 under 1550 nm excitation. The main emission peak is successfully tuned from green (NaYS2:Er3+) to highly efficient red luminescence (NaYS2:Er3+,Tm3+ and NaYS2:Er3+,Ho3+). The maximum red to green emission intensity ratio (IR/IG) increased by 31 and 80 times, respectively. The lifetime of 4I9/2(Er3+) level decreases significantly from 3432 s (NaYS2:Er3+) down to 636 (NaYS2:Er3+,Tm3+) and 423 s (NaYS2:Er3+,Ho3+), respectively. This is due to the altered energy transfer pathways, resulting in the population of 4F9/2(Er3+). The mechanisms of UCL and energy transfer processes are further supported by fluorescence decay dynamic measurements. The results show the realization of red-emissive UCL in the promising NaYS2 matrix, representing a new type of red UCL phosphors.

There is limited information on Tamarind production, trade and value added products in Kenya. Of late, there is growing interest in the domestic and export markets due to its multiple uses. The objective of this paper is to review existing literature to identify the missing value chain links which may help catalyse the scaling up of production and commercialization of Tamarind. Selected literature and interviews with traders and extension staff in the Coastal Counties were used to collect information for this study. Tamarind fruits are mainly collected in the wild in semi-arid areas of the country and marketed through informal channels. Mombasa is the terminal market for tamarind from Kenya and Uganda, from where domestic consumers and exporters obtain their supplies. Globally, virtually every part of tamarind tree (pulp, seed, leaves, flowers, bark and roots) has either nutritional, industrial or medicinal value. Tamarind fruit contains substantial levels of carbohydrates, proteins, vitamins, minerals and essential amino acids making it potentially useful in addressing wide spread malnutrition. Its anti-oxidant, anti-inflammatory, anti-microbial, anti-fungal and anti-diabetic properties gives tamarind wider application in conventional and traditional medicine. The findings suggest that scaling up the production and commercialization of tamarind in the country requires both public and private sector investment. The structure of this partnership should consist of the following value chain links: An efficient Seed / seedling system as a source of planting materials; production hubs by farmer associations, government institutions and private farms; aggregation centres for assurance of volumes and quality; processing, value addition and product diversification; an efficient distribution systems of wholesalers and retailers, particularly supermarkets; exporters and consumers. The agenda for further research should include breeding and availing early maturing cultivars demanded by the export market, quality management andvalue addition technologies.

To understand the possibility of interaction between scientific experimenting and artistic drawing in the form of drawings made in notebooks, assumed is a common element, a spiritual property, and transference as a mechanism for causality. Scientists, scientific experiments and artistic drawings all have different merits. Here the concept ‘speaking out’ in its meaning of expressiveness is proposed to bridge these differences. Scientific action and artistic action cannot be compared directly. However, a common spiritual element will make the investigated object, experimental set-up and notebook drawings comparable in the sense of translations authorized by the physicist. They all then speak out from the same source. In this paper considered are recent drawings made by physicists during experimentation, in notebooks and diaries. Discussed is transferal causality between the physicist, the artistic drawing and all the relevant objects belonging to the experiment. Spiritual properties are introduced for the physicist being a person, and for the investigated object, the experimental set-up and the drawings as objects.

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.

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.

Methane is a greenhouse gas and identified as a key driver of near-term climate change. Bottom-up approaches estimate annual methane loss from US natural gas production and transport at 6 Tg, but recent studies suggest this may be an underestimate. To investigate this possibility, an equipment-based emissions inventory, using EPA emission factors, was developed to calculate methane emissions from oil and gas operations in the Delaware Basin, USA. Emission factors and activity data were then updated using contemporary and region-specific measurement data. The original inventory estimated emissions at 315 Gg CH4 y-1 (gas production-normalized rate of 0.6% loss), while the updated inventory estimated emissions of 1,500 Gg CH4 y-1 (2.8% loss). The largest changes resulted from large fugitive emissions from oil production (+430 Gg CH4 y-1), updating maintenance activity emissions (+214 Gg CH4 y-1), considering flaring inefficiency (+174 Gg CH4 y-1), and the inclusion of associated gas venting (+136 Gg CH4 y-1). This study suggests that a systematic underestimate probably exists in current bottom-up inventories and identifies sources currently missing or may be incorrect. We also strongly recommend that emission factors should be validated through direct comparison against measurement campaigns that include long-tail distributions typical of oil and gas activities.

We consider two abstract systems of parabolic-hyperbolic type that model thermoelastic problems. We study the influence of the physical constants and the initial data on the nonexistence of global solutions that in our framework are produced by the blow-up in finite time of the norm of the solution in the phase space. We employ a differential inequality to find sufficient conditions that produce the blow-up. To that end, we construct a set that is positive invariant for any positive value of the initial energy. As a result we found that the coupling with the parabolic equation stabilizes the system, as well as the damping term in the hyperbolic equation. Moreover, for any pair of positive values $(\xi,\epsilon)$, there exist initial data such that the corresponding solution with initial energy $\xi$ blows-up at a finite time less than $\epsilon$. Our purpose is to improve results previously published in the literature.

Background: Data suggest that food allergies greatly impact a child’s health and growth due to inadequate nutrient intake. Our study aimed to establish the long-term outcome of children with food allergies compared to a control group. Methods: This study was a retrospective cohort study with longitudinal follow-up with a mean period of 4.85 years from the diagnosis to the last study visit. The patients’ nutritional intake was assessed using a three-day food diary and analysed by a dietitian. Patients (61 boys and 33 girls, mean age 6.9 years) had a single food allergy including 21 patients with cow’s milk, 34 with egg, and 39 with peanut allergies. The control group included 36 children (19 boys and 17 girls, mean age 8.03 years). Blood analysis was performed on all participants. Results: Data from our study showed that patients with cow’s milk, egg or peanut allergies had normal growth and achieved catch-up growth from the diagnosis until the last study visit. In the cow's milk allergy group, the allergy was shown to affect calcium intake (p<0.05), while egg and peanut allergies did not impact the dietary intake of nutrients. None of the investigated food allergies affected blood results (p<0.05). Conclusions: In the present study, we showed that single food allergies do not compromise growth in children if they are provided with appropriate support and that the affected children reach catch-up growth from the diagnosis.

The aim of this study was to assess the effects of three warm-up durations on post-exercise oxidative stress biomarkers, as well as recovery from the Wingate test in 14 amateur handball players. These players completed the Wingate test three times: after 5-min, after 10-min, and then after 15-min of warm-up, spread over two weeks with an interval of one week between each session. At the end of each session of the Wingate test, blood samples were taken: at rest (PR), after warming up (PWU) and immediately at the end of the test (P0), then after 10-min (P10) and after 20-min (P20) of recovery. The measured parameters are the total antioxidant status (TAS), superoxide dismutase (SOD), glutathione reductase (GR), glutathione peroxidase (GPx), malondialdehyde (MDA), protein-bound-carbonyls (PC) and thiobarbituric acid reactive substances (TBARS). The main findings revealed that anaerobic exercise induces oxidative stress, as evidenced by changes in antioxidant status and significant increases in concentrations of the majority of oxidative stress indicators (p < 0.05). At P20, plasma GPx, SOD, GR, TBARS, and PC contents, are lower and significantly different after a 15-min warm-up than after a 10-min or 5-min warm-up. ANOVA showed a significant ‘‘warm-up×recovery’’ effect on plasma GPx, SOD, GR, TBARS, and PC contents [F(8.104)=3.61; p<0.001; η2p=0.22, F(8.104): from 1.88 to 19.97; η2p: from 0.19 to 0.61: 0.05<p<0.001], respectively. We concluded that a 15-min warm-up was more efficient than the other duration in reducing free radical rise, emphasizing the importance of warm-up length on plasma oxidative stress biomarkers.

Convolution forms one of the most essential operations for the FPGA-based hardware accelerator. However, the existing designs often neglect the inherent architecture of FPGA, which puts forward an austere challenge on hardware resource requirements. Even though some previous works have proposed approximate multipliers or convolution acceleration algorithms to deal with this issue, the inevitable accuracy loss and resource occupation easily lead to performance degradation. Toward this, we first propose two kinds of resource-efficient optimized accurate multipliers based on LUTs or carry chains. Then targeting FPGA-based platforms, a generic multiply-accumulate structure is constructed by directly accumulating the partial products produced by our proposed optimized radix-4 Booth multipliers without intermediate multiplication and addition results. Experimental results demonstrate that our proposed multiplier achieves a maximum 51% look-up-table (LUT) reduction compared to the Vivado area optimized multiplier IP. Furthermore, the convolutional process unit using the proposed structure achieves a 36% LUT reduction compared to existing methods. As case studies, the proposed method is applied to DCT transformer and LeNet to achieves hardware resource saving without loss of accuracy.

The aim of this study is to test the effectiveness and safety of AE-AE surgery combining otoendoscopy and surgical microscopy in the treatment of acquired pars flaccida cholesteatoma in stages Ib and II of the Japan Otological Society classification occupying the tympanic cavity and attic. A historical cohort study on 65 patients. Of the total, 44 patients were treated by AE-AE surgery using an otoendoscope and 21 with canal wall-up tympanomastoidectomy (CWUT). Patients in whom the AE-AE technique was performed had a lower recurrence rate (9%) compared to those treated with CWUT (38%); p=0.013. In addition, the median time to recurrence was lower in the AE-AE group (4 years [P25-75= 1.25-2-75]) than in CWUT group (2 years [P25-75= 3.25-4.75]); p=0.048. Thresholds were higher in the CWUT group compared to the AE-AE group in pre-surgery (53±16 vs 44±15; p=0.039) and post-surgery (52±18 vs 42±16 dB dB; p=0.042), but not in pre-post-surgery comparisons neither in the AE-AE technique (p= 0.89) nor in the CWUT technique (p= 0.96). We concluded that AE with otoendoscopic support is an effective and safe technique for acquired cholesteatomas occupying box and attic in stages IB and II.

Trauma is one of the most common causes of death or permanent disability in young people, so a timely diagnostic approach is crucial. In polytrauma patients, CEUS has been shown to be more sensitive than US for the detection of solid organ injuries, improving the identification and grading of traumatic abdominal lesions with levels of sensitivity and specificity similar to those seen with MDCT. CEUS is recommended for the diagnostic evaluation of hemodynamically stable patients with isolated blunt moderate-energy abdominal traumas and for the diagnostic follow-up of conservatively managed abdominal traumas. In this pictorial review we illustrate the advantages and disadvantages of CEUS and the procedure details with tips and tricks during investigation of blunt moderate-energy abdominal trauma as well as during follow-up in non-operative management.

In this study, a new method for synthesizing Au-NaYF4:Yb3+/Er3+-DSPE-PEG2K nanocomposites was introduced. Using a hydrothermal method, the synthesized Yb3+- and Er3+-codoped NaYF4 upconversion luminescent materials and Au nanoparticles were doped into upconversion nanomaterials and modified with DSPE-PEG2k up-conversion nanomaterials. This material is known as Ag-UCNPs-DSPE-PEG2k, it improves both the luminous intensity because of the doped Au nanoparticles and has low cytotoxicity because of the DSPE-PEG2k modified. Exciting UCNPs with a wavelength of 980nm near-infrared light will emit light with a wavelength of 520nm to further excite gold nanoparticles to convert light energy into heat. Successful synthesized gold nanoparticles was confirmed using transmission electron microscopy (TEM). The morphology of UCNPs was observed using scanning electron microscopy (SEM), and the mapping confirmed the successful doping of Au nanoparticles. Fluorescence spectra were used to compare changes in luminescence intensity before and after doping Au nanoparticles. The cytotoxicity of Au-UCNPs-DSPE-PEG2K was tested via the cell counting kit-8 (CCK-8) method, and its imaging ability was characterized using the Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) method.

In this research paper, a novel high step up dc-dc converter with a coupled inductor and voltage doubler circuits is proposed. The converter achieves a high step up voltage gain with an appropriate duty ratio and low voltage stress on the power switches. Also, the energy which is stored in the leakage inductor of the coupled inductor can be recycled to the output. The operating principles and the steady state analysis of the proposed converter are discussed in detail. Finally, a prototype circuit of the proposed converter is implemented in the laboratory in order to verify the performance of the proposed converter.

Perovskite based solar cells have achieved tremendous progress reaching record efficiency in the past 5 years. Numerous new processes and chemistry have been reported and contribute to the perovskite rapid progress. Continuous efficiency improvements are still necessary for perovskite solar cells, and an exploratory data analysis on devices performance over multiple studies could boost the technology development. Such analysis could identify patterns or provide insights that are not obvious in a single study. Here we present a high quality dataset containing only independently certified Pb-based perovskite solar cells summarizing their efficiency, relevant I-V metrics, manufacturing processes and materials used. Analysis over the dataset provides insights on how aperture size, perovskite deposition methods and materials used in each functional layer affect the final solar cell efficiency and I-V metrics. Future directions are also suggested for efficiency improvements.

When animals evolve sufficient intelligence and dexterity to be able to learn to fabricate utility products (UPs) like tools, the UP's they produce become part of an induced-reproduction system that intrinsically shares many life-like traits with biological organisms, including genome-like fabrication and operation information that is physically-encoded in the animal fabricator’s neural networks. When this set of life- like traits includes a sufficient capacity for system-improving cultural evolution (UP-evolvability), the UPs become ‘para-alive’, i.e., nearly alive, or a form of non-biological UP-paralife that is equivalent to the life- status of biological viruses, plasmids, and transposons. In the companion paper I focus on the evolution of UP-paralife in the context of modern, language-capable humans and its predicted evolution going forward in time (Rice 2022). Here I look backward in time and focus on the origin of UP-paralife and its subsequent coevolution with human intelligence. I begin by determining the pathways leading to the evolution of large brains in the rare lineages of biological life that have sufficient intelligence to learn to fabricate tools –a critical first step in the evolution of UP-paralife. The simplest forms of these learning- based UPs, made by species like chimpanzees and New Caledonian crows, represent only proto-UP- paralife because they lack sufficient UP-evolvability. Expanded UP-evolvability required a combination of three attributes that enabled continuous niche-expansion of the animal fabricator via a new and advanced form of UP-mediated teamwork (TW): i) self-domestication that facilitated TW among low-related individuals, ii) learned volitional words (protolanguage) that represent ephemeral UPs that coordinate TW, and iii) learned fabrication of simple flaked-stone tools with cutting and chopping capabilities (a UP to make other structural UPs) that expanded teammate phenotypes and TW capabilities. This specific triad of attributes is synergistic because each one acts as a TW-enhancer that can gradually erode different components of the three major constraints on TW operation and expansion: too much selfishness, insufficient coordination signals, and insufficient physical traits of teammates. The increase in UP- evolvability was transformative and marked the origin of UP-paralife and the initiation of coevolution between UP-paralife (cultural evolution) and the intelligence of its hominin/human symbiont (genetic evolution) that fostered 2.5 million years of: i) continuous brain size increase and niche-expansion within the genus Homo, and ii) parallel advances in the diversity, complexity and uses of UP-paralife. This coevolution also fostered evolutionary expansion of word-based communication, and eventually language, that acted in a catalyst-like manner to facilitate the evolution of increasingly complex forms of imagination, reasoning, mentalizing, and UP-generating technology. I next focus on the evolution of creativity in the human lineage –in the form of divergent thinking and creative imagination. I conclude that the evolution of this advanced cognitive feature required a preadaptation of sufficient intelligence and is the component of human cognition that was the major causal factor generating the greatly expanded diversity and complexity of UP-paralife currently associated with modern humans. Lastly, I apply my findings to the issue of the prevalence of extraterrestrial intelligent life. I conclude that any exoplanets with detected chemical life will very rarely (e.g., probability ~10-5 for a planet closely matching Earth’s characteristics) have evolved intelligence equalling or exceeding that of humans.

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.

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.

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.

Like the most of crops, especially sugarcane needs to be kept upright until it is harvested. Because, lodging of sugarcane has a significant negative effect on cane yield and sugar content of sugarcane. To keep sugarcane upright earthing up is an utmost intercultural operation. In Bangladesh, most of the operations for sugarcane cultivation including earthing up are generally done in traditional method using human labor which increases production costs as well as reduces the income of sugarcane growers. So, a cost effective two-wheeled tractor (2WT) mounted earthing up machine was developed in Bangladesh Sugarcrop Research Institute (BSRI), Pabna to reduce drudgery and cost of sugarcane production. Field tests were conducted in experimental sugarcane field at BSRI and technical and economic performances of the developed earthing up machine were also carried out based on the field test. Average effective field capacity and field efficiency were found 0.16 ha/hr and 77.41 %, respectively for the developed earthing up machine. The earthing up machine was not found economically viable when it is used only for earthing up operation. Besides, when the 2WT was being used as the main driver for other activities including earthing up operation, then the earthing up machine becomes economically beneficial with net cash flow, net present value, internal rate of return, benefit-cost ratio and payback period of BDT 148497/ha, BDT 23184, 3%, 3.81:1 and approximately 1 year, respectively. On the contrary, considering cost of only earthing up tool without 2WT, then it was found economically beneficial with net cash flow, internal rate of return, net present value, benefit-cost ratio and payback period of BDT 16428/ha, BDT 3053, 4.7%, 2.71:1 and approximately 2 years, respectively. Since 2WT is commonly used for versatile farming purposes. Therefore, versatile use of 2WT as prime mover for other machines including earthing up machine can make earthing up machine economically viable and beneficial for sugarcane growers.

The fixed stroke fatigue test can not simulate the real load condition of the landing gear, and the variable stroke fatigue test has increasingly become the mainstream. A modular test device for fatigue test for aircraft landing gears was designed, the automatic control of the buffer stroke was realized with ectopic displacement control technology effectively, the horizontal loading cylinder could follow the wheel axis actively with the displacement active control technology, follow-up loading along the vertical direction was realized through constructing a polar coordinate system and making the load line automatic alignment, a split-type dummy wheel was designed to improve the loading accuracy, a booster cylinder was designed to realize the control loading of high pressure combined with the hydraulic control system. The test shows that the technical scheme meets the design requirements, and the fatigue test was completed successfully. The device for landing gear variable stroke fatigue testing can be applied to other landing gear static / fatigue tests.

In this note, we provide some effective treatments of a general linear model with adding-up restrictions via algebraic operations of given vectors and matrices in the model, including analytic expressions of the well-known ordinary least-squares estimator (OLSE) and the best linear unbiased estimator (BLUE) of the unknown parameters in the model.

Background This paper draws learnings and successes based on field implementation experience spanning four years (2016–2019) of implementing CLTS by Prince of Peace Orphans and Widows Vision, a community-based organization located in Kaberamaido district in eastern Uganda. Methods The study aimed to document, disseminate and inform from an evidence-based point of view how adaptation and collaborative engagements triggers learning from mistakes to inform iterative changes from improvements and success.This is a descriptive paper that used project documents review based on field implementation experience. Existing project reports were synthesised, collated and curated for evidence. Data were drawn from project reports and records to inform narratives in writing. Implementation of the project was executed in homogenous rural communities occupied by people of the same dialect, cultural and social settings. Results We note that success in CLTS implementation can hardly be achieved by merely following prescriptions in handbooks and guidelines but rather by devising innovative community engagement and other participatory and community-driven techniques that foster adaptive management, promote ownership, and buy-in.Having learned from our failures, we used data to inform decisions and transformatively deviated from traditional CLTS implementation and introduced high impact and innovative approaches such as the use of CLTS helpdesks and Situation room, the Pamoja approach, learning labs and iterative feedback loops, innovatively tackling slippage and carefully introducing the follow-up mandona approach. These enhanced experiential learning and ultimately resulted in sustained sanitation behaviour. Conclusions The CLTS approach as outlined in the handbook needs to be flexibly adapted to address contextual needs. Reflective and learning sessions reinforced with routine feedback loops from implementers and beneficiaries yields tremendous results, propagates experiential learning, and ultimately results in a transformative deviation from undesirable to desired sanitation behaviours. These innovative approaches once carefully blended have proved to be sustainable, are adaptable and can work in an even larger scale and in a variety of contexts.

Recently, we have witnessed a booming development of composites and multi-dopant metal oxides to be employed as novel photocatalysts. Yet the practical application of photocatalysis for environmental purposes is still elusive. Concerns about the unknown fate and toxicity of nanoparticles, unsatisfactory performance in real conditions, mass transfer limitations and durability issues have so far discouraged investments in full-scale applications of photocatalysis. Herein, we provide a critical overview of the main challenges that are limiting large-scale application of photocatalysis in air and water/wastewater purification. We then discuss the main approaches reported in the literature to tackle these shortcomings, such as the design of photocatalytic reactors that retain the photocatalyst, the study of degradation of micropollutants in different water matrices, and the development of gas-phase reactors with optimized contact time and irradiation. Furthermore, we provide a critical analysis of research-practice gaps such as treatment of real water and air samples, degradation of pollutants with actual environmental concentrations, photocatalyst deactivation, and cost and environmental life-cycle assessment.

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 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.

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.

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.

Newborn screening (NBS) is hailed as a public health success, but little is known about what happens to these children after diagnosis. There has been difficulty gathering long-term follow-up (LTFU) data consistently, reliably, and with minimal effort by state NBS programs from clinicians. Six programs have been working towards a core minimal LTFU dataset, starting with data elements proposed by the Advisory Committee on Heritable Disorders in Newborns and Children’s Follow-up and Treatment workgroup (ACHDNC FUTR). This minimal data set could begin to address the impact of early diagnosis by NBS. After three rounds of data collection and revision to a data collection tool that defines the minimal LTFU data elements, the group agreed that it was most important for the dataset to capture two items for the denominator--diagnosis and if the child moved or died—with three outcomes: if the child was still alive, if the child had contact with a specialist, and if they received appropriate care specific to their diagnosis within the year. All six programs were able to provide these data. In 2022, about 83.8% (563/672) of the children in these LTFU programs were alive, 92.0% saw a specialist, and 87.7% received appropriate care.

Background. There are few studies about post myocardial infarction follow-up using telemedicine. We organized a post-discharge telemedicine service with a dedicated team. To do this, it was necessary that all stakeholders involved in the organization and use of the telemedicine service were properly educated and informed. Methods. We designed a theoretical-practical mini-course, to train healthcare personnel and increase skills, with excellent learning outcomes and satisfaction. Thereafter, we enrolled patients affected by acute myocardial infarction with ST elevation (STEMI), MINOCA (myocardial infarction with no obstructive coronary atherosclerosis), Takotsubo syndrome or spontaneous coronary dissection, and high-risk acute myocardial infarction without ST elevation (NSTEMI). At discharge, the cardiology technician performed counselling for the patient; using regional platforms, televisit at 1 and 4 months monitored major adverse cardiac events (MACE), heart failure, arrhythmias, unstable angina and non-cardiovascular events, therapy adherence, target therapy and customer satisfaction. Results. Between November 2021 and February 2023, we enrolled 110 patients: 72% affected by STEMI, 22% by NSTEMI. At the 1-month follow up, 12 patients did not reach pressure target and 23 patients did not reach LDL target. We observed three in hospital readmission, three in hospital visits for further investigation and one death. To date, a four months follow up was performed for 54 patients. No readmissions or deaths occurred. We detect a rate of 96% of customer satisfaction. Conclusion. A health coordination center with a dedicated team makes televisit safe as a follow-up for post myocardial infarction patients. Before, it is fundamental for healthcare professionals the acquisition of theoretical knowledge and updates and the acquisition of manual, technical and practical skills.

Graphene is a two-dimensional nanostructured material with intrinsic properties that show promising performance in various applications, including electronics, renewable energy, medicine, mechanical enforcement and others. This study reports a bottom-up approach for the conversion of cyclohexane into graphene nanoflakes, which were then deposited onto fiberglass using a non-thermal generator. The composite was characterized using transmission electron microscopy, which revealed the formation of stacked few-layer graphene with a partially disordered structure and a d-spacing of 0.358 nm between the layers. X-ray diffraction confirmed the observations from the TEM images. SEM images showed the agglomeration of carbonaceous material onto the fiberglass, which experienced some delamination due to the synthesis method. Raman spectroscopy indicated that the obtained graphene exhibited a predominance of defects in its structure. Additionally, Atomic Force Microscopy (AFM) analyses revealed the formation of graphene layers with varying levels of porosity.

Many pigments and dyes are not only valuable molecules in manufacturing, but also pollutants. Science has certainly been producing sustainable alternatives to counter the issue of dyes, but this is not enough. There is also a need for change in the business attitude and leadership approach of the organizations that operate in the industry. Only through the successful combination of new technologies and forward-looking decision making, it will be possible to alter the status quo and deal with the multiple environmental and supply chain challenges that these businesses are and will be facing. With a focus on colorants, this critical review article will reflect on the importance of both a biotechnological approaches and sustainable leadership to achieve a sustainable, forward-looking fashion industry.

The way Norway is spearheading electrification in the transport sector is of global interest, and details of its policies and emission impacts represent a reference. We used the NERVE model, a bottom-up high-resolution traffic emission model, to calculate all exhaust emissions in Norway (2009-2020). This allows us to evaluate the co-benefit of policies to target climate change mitigation and air quality. We have analyzed local municipal data with regards to traffic growth, road network influences, vehicle composition, emissions and energy consumption. Light vehicle CO2 emissions per kilometre have been reduced by 22% since 2009, mainly driven by an increaseing bio-fuel mixing and BEV share. BEVs are mostly located in and around the main cities, areas with young vehicle fleets, and strong local incentives. Beneficiaries of all BEVs incentives have been a subset of the population with strong economic indicators. The incentivized growth in the share of diesel-fuelled passenger vehicle has been turned, and together with Euro6 emission standards, light vehicle NOx emissions have been halved since peaking in 2014. BEV represent an investment in emission reductions in years to come and current sales set up for an accelerated decline in emissions despite growth in traffic.

Protein translational modifications (PTMs) generate an enormous, but as yet undetermined, expansion of the expressed proteoforms. In this Viewpoint, we firstly differentiate the concepts of proteoform and peptidoform by reviewing and discussing previous literature. We show that the current PTM biological investigation and annotation largely follow a PTM site-specific rather than proteoform-specific approach. We further illustrate a potentially useful matching strategy in which a particular “modified peptidoform” is matched to the corresponding “unmodified peptidoform” as a reference for the quantitative analysis between samples and conditions. We suggest this strategy could provide directly relevant information for learning the PTM site-specific biological functions. Accordingly, we advocate for the wider use of the nomenclature “peptidoform” in the future bottom-up proteomic studies.

Education on Innovation and Entrepreneurship (I&E) has increased in the last two decades, specially, through MOOCs. Lately, these reusable online alternatives have tended to be revalorized by HEIs into blended learning activities, posing new challenges for instructors, specially, on how to bridge prior knowledge with in-class activities. Adopting a discursive approach to knowledge, our proposal aims to meet this challenge by identifying student’s ‘representations’, i.e., patterned constructions on disciplinary knowledge. Representations can be found across different cohorts and thus further complemented by instructors. To test this assumption and build our proposal, we analysed student’s representations in two observations. We mapped students’ representations over key I&E definitions (e.g., ‘start-up’) and, to know how prior knowledge may be complemented by instructors, we identified students’ alignment with expert disciplinary knowledge. Firstly, we found that the two cohorts tended to express representations by turning attention to several dimensions, e.g., referring to different types of features or finalities associated with concepts. Secondly, the disciplinary alignment description revealed that students tended to focus on the same components present in experts’ definitions, but with a greater level of generality. Our results have been packaged into a proposal that aims to help instructors scale their blended activities.

As fossil-fueled heating in the building sector is responsible for 16 % of Germanys total CO2 emissions, it is of great importance to use climate-neutral alternatives for the decarbonization of this sector. Options for the climate-neutral heating of buildings include electricity or hydrogen as energy carriers, both explicitly considered by German policy. In this paper, bottom-up studies are conducted to investigate the role of hydrogen for the climate-neutral energy supply of ten selected residential buildings in comparison to electricity-based systems. Based on a selection of as different typical buildings as possible for single- (SFH) and multi-family houses (MFH) of different construction years, demand profiles are simulated for each building and the respective cost-optimal supply system is determined. For the construction of this system, the electricity-based technologies available are electric heater and heat pump as well as the hydrogen-based technologies hydrogen boiler and fuel cell combined heat and power (CHP) system. Based on the results of the optimization, sensitivity analyses are performed. These analyses aim to identify threshold values of the hydrogen price for the use of hydrogen in building energy systems as well as to make the quantities of hydrogen consumed visible.The identified threshold values show the significant role of hydrogen-operated CHP in MFH if the hydrogen price reaches 0.17 €/kWh_H2 in 2050 at an electricity price of 0.31 €/kWh_el. So, hydrogen-based energy systems represent an economically viable alternative to electricity-based systems with heat pumps. We identify electricity to hydrogen price ratios for the economically viable use of hydrogen in the examined buildings that range from 1.67 to 2.82. According to these ratios, the economically reasonable use of hydrogen in buildings can be derived. For the individual building groups for the year 2050, a ratio of 2.5 can be determined for SFH and 1.8 for MFH, that is favored by the use of CHP which also supplies electricity to the buildings. However, the role hydrogen will finally play in German residential buildings in the future depends to a large extent on political decisions on distribution issues and price signals.

Cancer is one of the leading causes of death and morbidity with a complex pathophysiology. Traditional cancer therapies include chemotherapy, radiation therapy, targeted therapy, and immunotherapy. However, limitations such as lack of specificity, cytotoxicity, and multi-drug resistance pose a substantial challenge for favorable cancer treatment. The advent of nanotechnology has revolutionized the arena of cancer diagnosis and treatment. Nanoparticles (1-100nm) can be used in the treatment of cancer owing to their specific advantages such as biocompatibility, reduced toxicity, more excellent stability, enhanced permeability and retention effect, and precise targeting. Nanoparticles are classified into several main categories. The nanoparticle drug delivery system is particular and utilizes tumor and tumor environment characteristics. Nanoparticles not only solve the limitations of conventional cancer treatment but also overcome multidrug resistance. Additionally, as new multidrug resistance mechanisms are unraveled and studied, nanoparticles are being investigated more vigorously. Various therapeutic implications of nano-formulations have created brand new perspectives for cancer treatment. However, a majority of the research is limited to in vivo and in vitro studies, and the number of nano-drugs that are approved has not much amplified over the years. In this review, we discuss numerous types of nanoparticles, targeting mechanisms along with approved nanotherapeutics for oncological implications in cancer treatment. Further, we also summarize the current perspective, advantages, and challenges in clinical translation.

(1) Background: The aim of the present study was to evaluate the effect of different types of warm-ups on the strength and skin temperature of Paralympic powerlifting athletes; (2) Methods: The participants were 15 male Paralympic powerlifting athletes. It was analyzed the effects of three different types of warm-up (without warm-up (WW), traditional warm-up (TW), or stretching warm-up (SW)) on static and dynamic strength tests as well as in the skin temperature, which was monitored by thermal imaging; (3) Results: show no differences in the dynamic and static indicators of the force in relation to the different types of warm-up. No significant differences were found in relation to the Peak Torque (p = 0.055, F=4.560, η2p= 0.246 medium effect), and 1-Repetition Maximum (p = 0.139, F=3.191, η2p = 0.186, medium effect) between the different types of warm-up. In the thermographic analysis, there was a significant difference only in the Pectoral muscle clavicular portion between the TW (33.04 ± 0.71ºC) and the WW (32.51 ± 0.74ºC) (p = 0.038). The TW method also presented slightly higher values than the SW and WW in the Pectoral Muscles Sternal portion and in the Deltoid anterior portion, but with p-value &gt; 0.05; (4) Conclusions: that the types of warm-up studied do not seem to interfere with the performance of Paralympic Powerlifting athletes. However, the thermal images showed that traditional warm-up best meets the objectives expected for this preparation phase.

Long-term, high-accuracy mapping of built-up land dynamics is essential for understanding urbanization and its consequences for the environment. Despite advances in remote sensing and classification algorithms, built-up land mapping using early satellite imagery (e.g., from the 2000s and earlier) remains prone to uncertainty. We mapped the extent of built-up land in the North China Plain, one of China’s most important agricultural regions, from 1990 to 2019 at three-year intervals. Using dense time-stack Landsat data, we applied discrete Fourier transformation to create temporal predictors and reduce mapping uncertainties for early years. We improved overall accuracy by 8% compared to using spectral and indices predictors alone. We implemented a temporal correction algorithm to remove inconsistent pixel classifications, further improving accuracy to a consistently high level (>94%) across years. A cross-product comparison showed that our study achieved the highest levels of accuracy across years. Total built-up land in the North China Plain increased from 37,941 km² in 1990–1992 to 131,578 km² in 2017–2019. Consistent, high-accuracy built-up land mapping provides a reliable basis for policy planning in one of the most rapidly urbanizing regions of the planet.

Cortical actomyosin flows, among other mechanisms, scale up spontaneous symmetry breaking and thus play pivotal roles in cell differentiation, division, and motility. According to many model systems, myosin motor-induced local contractions of initially isotropic actomyosin cortices are nucleation points for generating cortical flows. However, the positive feedback mechanisms by which spontaneous contractions can be amplified towards large-scale directed flows remain mostly speculative. To investigate such a process on spherical surfaces, we reconstituted and confined initially isotropic minimal actomyosin cortices to the interfaces of emulsion droplets. The presence of ATP leads to myosin-induced local contractions that self-organize and amplify into directed, large-scale actomyosin flows. By combining our experiments with theory, we found that the feedback mechanism leading to a coordinated, directional motion of actomyosin clusters can be described as asymmetric cluster vibrations, caused by intrinsic non-isotropic ATP consumption, in conjunction with spatial confinement. By tracking individual actomyosin clusters, we identified fingerprints of vibrational states as the basis of directed motions. These vibrations may represent a generic key driver of directed actomyosin flows under spatial confinement in vitro and in living systems.

Purpose: To evaluate seasonal variation in intra-ocular pressure (IOP) with and without short tear break-up time (SBUT, BUT ≤ 5 s). Methods: This study enrolled 176 patients who visited one of six eye clinics in Japan for IOP measurement at every season. The mean patient age was 67.9 years, including 79 males. We compared the seasonal variation in IOP (mean ± SD) across spring (Mar-May), summer (Jun-Aug), fall (Sep-Nov), and winter (Dec-Feb). Results: The IOP (mmHg) in winter and summer, respectively, was 12.8 ± 3.7 and 12.8 ± 3.1 for non-glaucoma patients without SBUT (n = 47, P = 0.964), 14.8 ± 3.4 and 13.3 ± 3.4 for non-glaucoma patients with SBUT (n = 57, P < 0.001), 14.3 ± 3.2 and 14.1 ± 3.4 for glaucoma patients without SBUT (n = 36, P = 0.489), and 13.3 ± 3.0 and 11.6 ± 2.9 for glaucoma with SBUT (n = 36, P < 0.001). Seasonal variation was largest across the seasons in the glaucoma with SBUT group, and the magnitude of seasonal variation correlated with BUT (β = 0.228, P = 0.003). Conclusions: Seasonal variation tended to be larger in patients with SBUT than those without SBUT.

Photoelectrochemical systems have the potential to contribute to de-carbonation of the energy system because solar energy can be directly converted to hydrogen which can be burnt without the release of greenhouse gases. However, meaningful deployment of photoelectrochemical (PEC) technology in the global energy system, even when highly efficient scaled up devices become available, shall only be a reality when their safe and reliable operation can be guaranteed over serveral years of service life. The first part of the review discusses the importance of hermetic sealing of up scaled PEC device provided by the casing and sealing joints from a reliability and risk perspective. The second part of the review presents a survey of fully functional devices and early demonstrators and uses this to establish the extent to which the state of the art in PEC device design address the issue of hermetic sealing. The survey revealed that current material choices and sealing techniques are still unsuitable for scale –up and commercialization. Accordingly, we examined possible synergies with related photovoltaic and electrochemical devices that have been commericalised and derived therefrom, recommendations for future research routes that could accelerate the development of hermetic sealings of PEC devices.

Up-conversion nanoparticles (UCNP) under near-infrared (NIR) light irradiation have been well investigated in the field of bio-imaging. However, the low up-conversion luminescence (UCL) intensity limits applications. Plasmatic modulation has been proposed as an effective tool to adjust the luminescence intensity and lifetime. In this study discrete dipole approximation (DDA) was explored concerning guiding the design of UCNP@mSiO2-Au structures with enhanced UCL intensity. The extinction effects of gold shells could be changed by adjusting the distance between the UCNPs and the Au NPs by synthesized tunable mesoporous silica (mSiO2) spacers. Enhanced UCL was obtained under 808 nm irradiation. Theoretical predictions could not be demonstrated to full extend by experimental data, indicating that better models for simulation need to better take into account inhomogeneities in particle morphologies.

The conventional rolled-up model for carbon nanocones assumes that the cone is constructed from a rolled-up graphene sheet joined seamlessly, which predicts five distinct vertex angles. This model completely ignores any effects due to the changing curvature and all bond lengths and bond angles are assumed to be those for the planar graphene sheet. Clearly curvature effects will become more important closest to the cone vertex, and especially so for the cones with the smaller apex angles. Here we construct carbon nanocones which in the assembled cone are assumed to comprise bond lengths and bond angles which are, as far as possible, equal throughout the structure at the same distance from the conical apex. Predicted bond angles and bond lengths are shown to agree well with those obtained by relaxing the conventional rolled-up model using the LAMMPS software. The major objective here is not simply to model physically realisable carbon nanocones for which numerical procedures are far superior, but rather to produce an improved model that takes into account curvature effects close to the vertex, and from which we may determine an analytical formula which represents an improvement on that for the conventional rolled-up model.

Recent progress in the fabricating flexible electronics has been developed significantly due to the increased interest of the flexible electronics which can be applied to enormous fields not only to conventional electronic devices but to bio/eco electronic devices. Flexible electronics can be applied to wide range of fields such as flexible display, flexible power storage, flexible solar cells, wearable electronics and healthcare monitoring devices. Recently, flexible electronics are being attached on the skin and even implanted into human body to monitor the bio-signals and for treatment purpose. To improve the electrical characteristic and the mechanical properties of flexible electronics, nanoscale fabrications using novel nano-materials are required. Advanced in nanoscale fabrication methods allow construction of the active materials that can combine with the ultra-thin soft substrate to form flexible electronics with high performances and reliability. In this review, wide range of nanoscale fabrication methods for flexible electronics classified in either top-down or bottom-up approaches such as conventional photolithography, soft lithography, nanoimprint lithography, growth, assembly and chemical vapor deposition(CVD) will be reported with specific fabrication processes and results. Here, our aim is to introduce various fabrication methods that can be used to fabricate the flexible electronics.

In this paper, we study the fractional pseudo-parabolic equations u_t + (−△)^s u + (−△)^s u_t = u log |u|. Firstly, we recall the relationship between the fractional Laplace operator (−△)^s and the fractional Sobolev space H^s and discuss the invariant sets and the vacuum isolating behavior of solutions with the help of a family of potential wells. Then, we derive a threshold result of existence of global weak solution: for the low initial energy J(u₀) < d, the solution is global in time with I(u₀) > 0 or ∥u₀∥X_0(Ω) = 0 and blows up +∞ with I(u₀) < 0; for the critical initial energy J(u₀) = d, the solution is global in time with I(u₀) ≥ 0 and blows up at +∞ with I(u₀) < 0. The decay estimate of the energy functional for the global solution is also given.

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.