Band Data Research Articles

Big data analysis based on the correlation between live-streaming with goods, perceived value and consumer repurchase

Abstract Clarifying the correlation between live banding, perceived value, and consumer repurchase is to enhance the economic benefits of live banding better. In this paper, a K-nearest neighbor classification algorithm is proposed in the context of big data analysis technology, and the principle and distance criterion of the algorithm are explained. Then the KNN algorithm is optimized using the Gaussian kernel density function, and the optimization process of the algorithm is given. Finally, the optimized KNN algorithm is used to mine and analyze the indicators of Taobao live banding data, and the performance evaluation is also done for the algorithm. Regarding live-streaming with goods and sensory value, the ratings of A, B, C and D accounted for 38.41%, 36.73%, 34.54% and 35.4%, respectively. In terms of the association between live banding and consumer repurchase, the average value of the data is 33.46%, the maximum value is 43.65%, and the minimum value is 18.16%. Big data analysis shows a strong correlation between live banding, sensory value and consumer repurchase. Live banding enhances consumer sensory value, sensory value influences consumer repurchase, and consumer repurchase behavior promotes live banding optimization, which continuously improves the quality of the product and marketing atmosphere of live banding.

Comparative Analysis of Machine and Deep Learning Models for Soil Properties Prediction from Hyperspectral Visual Band

Estimating various properties of soil, including moisture, carbon, and nitrogen, is crucial for studying their correlation with plant health and food production. However, conventional methods such as oven-drying and chemical analysis are laborious, expensive, and only feasible for a limited land area. With the advent of remote sensing technologies like multi/hyperspectral imaging, it is now possible to predict soil properties non-invasive and cost-effectively for a large expanse of bare land. Recent research shows the possibility of predicting those soil contents from a wide range of hyperspectral data using good prediction algorithms. However, these kinds of hyperspectral sensors are expensive and not widely available. Therefore, this paper investigates different machine and deep learning techniques to predict soil nutrient properties using only the red (R), green (G), and blue (B) bands data to propose a suitable machine/deep learning model that can be used as a rapid soil test. Another objective of this research is to observe and compare the prediction accuracy in three cases i. hyperspectral band ii. full spectrum of the visual band, and iii. three-channel of RGB band and provide a guideline to the user on which spectrum information they should use to predict those soil properties. The outcome of this research helps to develop a mobile application that is easy to use for a quick soil test. This research also explores learning-based algorithms with significant feature combinations and their performance comparisons in predicting soil properties from visual band data. For this, we also explore the impact of dimensional reduction (i.e., principal component analysis) and transformations (i.e., empirical mode decomposition) of features. The results show that the proposed model can comparably predict the soil contents from the three-channel RGB data.

MDvT: introducing mobile three-dimensional convolution to a vision transformer for hyperspectral image classification

ABSTRACT Hyperspectral images carry numerous spectral bands, and their wealth of band data is a valuable source of information for the accurate classification of ground objects. Three-dimensional (3D) convolution, although an excellent spectral information extraction method, is limited by its huge number of parameters and long model training time. To allow better integration of 3D convolution with the most popular transformer models currently available, a new architecture called mobile 3D convolutional vision transformer (MDvT) is proposed. The MDvT introduces inverted residual structure to reduce the number of model parameters and balance the data mining efficiency of low-dimensional data input. Simultaneously, a square patch is used to cut the sequence of tokens to accelerate the model operation. Through extensive experiments, we evaluated the classification overall performance of the proposed MDvT on the WHU-Hi and Pavia University datasets, and demonstrated significant improvements in classification accuracy and model runtime compared with classical deep learning models. It is worth noting that compared with directly integrating 3D convolution into the transformer model, the MDvT architecture improves the accuracy while reducing the time to train an epoch by approximately 58.54%. To facilitate the reproduction of the work in this paper, the model code is available at https://github.com/gloryofroad/MDvT.

Population dynamics and harvest management of eastern mallards

AbstractManaging sustainable harvest of wildlife populations requires regular collection of demographic data and robust estimates of demographic parameters. Estimates can then be used to develop a harvest strategy to guide decision‐making. Mallards (Anas platyrhynchos) are an important species in the Atlantic Flyway for many users and they exhibited exponential growth in the eastern United States between the 1970s and 1990s. Since then, estimates of mallard abundance have declined 16%, prompting the Atlantic Flyway Council and United States Fish and Wildlife Service to implement more restrictive hunting regulations and develop a new harvest strategy predicated on an updated population model. Our primary objective was to develop an integrated population model (IPM) for use in an eastern mallard harvest management strategy. We developed an IPM using annual estimates of breeding abundance, 2‐season banding and recovery data, and hunter‐harvest data from 1998 to 2018. When developing the model, we used novel model selection methods to test various forms of a sub‐model for survival including estimating the degree of harvest additivity and any age‐specific trends. The top survival sub‐model included a negative annual trend on juvenile survival. The IPM posterior estimates for population abundance tracked closely with the observed estimates and estimates of mean annual population growth rate ranged from 0.88 to 1.08. Our population model provided increased precision in abundance estimates compared to survey methods for use in an updated harvest strategy. The IPM posterior estimates of survival rates were relatively stable for adult cohorts, and annual growth rate was positively correlated with the female age ratio, a measure of reproduction. Either or both of those demographic parameters, juvenile survival or reproduction, could be a target for management efforts to address the population decline. The resulting demographic parameters provided information on the equilibrium population size and can be used in an adaptive harvest strategy for mallards in eastern North America.

Applied Machine-Learning Models to Identify Spectral Sub-Types of M Dwarfs from Photometric Surveys

M dwarfs are the most abundant stars in the Solar Neighborhood and they are prime targets for searching for rocky planets in habitable zones. Consequently, a detailed characterization of these stars is in demand. The spectral sub-type is one of the parameters that is used for the characterization and it is traditionally derived from the observed spectra. However, obtaining the spectra of M dwarfs is expensive in terms of observation time and resources due to their intrinsic faintness. We study the performance of four machine-learning (ML) models—K-Nearest Neighbor (KNN), Random Forest (RF), Probabilistic Random Forest (PRF), and Multilayer Perceptron (MLP)—in identifying the spectral sub-types of M dwarfs at a grand scale by deploying broadband photometry in the optical and near-infrared. We trained the ML models by using the spectroscopically identified M dwarfs from the Sloan Digital Sky Survey (SDSS) Data Release (DR) 7, together with their photometric colors that were derived from the SDSS, Two-Micron All-Sky Survey, and Wide-field Infrared Survey Explorer. We found that the RF, PRF, and MLP give a comparable prediction accuracy, 74%, while the KNN provides slightly lower accuracy, 71%. We also found that these models can predict the spectral sub-type of M dwarfs with ∼99% accuracy within ±1 sub-type. The five most useful features for the prediction are r − z, r − i, r − J, r − H , and g − z, and hence lacking data in all SDSS bands substantially reduces the prediction accuracy. However, we can achieve an accuracy of over 70% when the r and i magnitudes are available. Since the stars in this study are nearby (d ≲ 1300 pc for 95% of the stars), the dust extinction can reduce the prediction accuracy by only 3%. Finally, we used our optimized RF models to predict the spectral sub-types of M dwarfs from the Catalog of Cool Dwarf Targets for the Transiting Exoplanet Survey Satellite, and we provide the optimized RF models for public use.

A Management Method of Multi-Granularity Dimensions for Spatiotemporal Data

To understand the complex phenomena in social space and monitor the dynamic changes in people’s tracks, we need more cross-scale data. However, when we retrieve data, we often ignore the impact of multi-scale, resulting in incomplete results. To solve this problem, we proposed a management method of multi-granularity dimensions for spatiotemporal data. This method systematically described dimension granularity and the fuzzy caused by dimension granularity, and used multi-scale integer coding technology to organize and manage multi-granularity dimensions, and realized the integrity of the data query results according to the correlation between the different scale codes. We simulated the time and band data for the experiment. The experimental results showed that: (1) this method effectively solves the problem of incomplete query results of the intersection query method. (2) Compared with traditional string encoding, the query efficiency of multiscale integer encoding is twice as high. (3) The proportion of different dimension granularity has an impact on the query effect of multi-scale integer coding. When the proportion of fine-grained data is high, the advantage of multi-scale integer coding is greater.

The optical behaviour of BL Lacertae at its maximum brightness levels: a blend of geometry and energetics

ABSTRACT In 2021 BL Lacertae underwent an extraordinary activity phase, which was intensively followed by the Whole Earth Blazar Telescope (WEBT) Collaboration. We present the WEBT optical data in the BVRI bands acquired at 36 observatories around the world. In mid-2021 the source showed its historical maximum, with R = 11.14. The light curves display many episodes of intraday variability, whose amplitude increases with source brightness, in agreement with a geometrical interpretation of the long-term flux behaviour. This is also supported by the long-term spectral variability, with an almost achromatic trend with brightness. In contrast, short-term variations are found to be strongly chromatic and are ascribed to energetic processes in the jet. We also analyse the optical polarimetric behaviour, finding evidence of a strong correlation between the intrinsic fast variations in flux density and those in polarization degree, with a time delay of about 13 h. This suggests a common physical origin. The overall behaviour of the source can be interpreted as the result of two mechanisms: variability on time-scales greater than several days is likely produced by orientation effects, while either shock waves propagating in the jet, or magnetic reconnection, possibly induced by kink instabilities in the jet, can explain variability on shorter time-scales. The latter scenario could also account for the appearance of quasi-periodic oscillations, with periods from a few days to a few hours, during outbursts, when the jet is more closely aligned with our line of sight and the time-scales are shortened by relativistic effects.

CURTAINs for your sliding window: Constructing unobserved regions by transforming adjacent intervals.

We propose a new model independent technique for constructing background data templates for use in searches for new physics processes at the LHC. This method, called Curtains, uses invertible neural networks to parameterise the distribution of side band data as a function of the resonant observable. The network learns a transformation to map any data point from its value of the resonant observable to another chosen value. Using Curtains, a template for the background data in the signal window is constructed by mapping the data from the side-bands into the signal region. We perform anomaly detection using the Curtains background template to enhance the sensitivity to new physics in a bump hunt. We demonstrate its performance in a sliding window search across a wide range of mass values. Using the LHC Olympics dataset, we demonstrate that Curtains matches the performance of other leading approaches which aim to improve the sensitivity of bump hunts, can be trained on a much smaller range of the invariant mass, and is fully data driven.

Confirmation and Keplerian motion of the gap-carving protoplanet HD 169142 b

ABSTRACT We present the re-detection of a compact source in the face-on protoplanetary disc surrounding HD 169142, using VLT/SPHERE data in YJH bands. The source is found at a separation of 0${_{.}^{\prime\prime}}$319 (∼37 au) from the star. Three lines of evidence argue in favour of the signal tracing a protoplanet: (i) it is found in the annular gap separating the two bright rings of the disc, as predicted by theory; (ii) it is moving at the expected Keplerian velocity for an object at ∼37 au in the 2015, 2017, and 2019 data sets; and (iii) we also detect a spiral-shaped signal whose morphology is consistent with the expected outer spiral wake triggered by a planet in the gap, based on dedicated hydrodynamical simulations of the system. The YJH colours we extracted for the object are consistent with tracing scattered starlight, suggesting that the protoplanet is enshrouded in a significant amount of dust, as expected for a circumplanetary disc or envelope surrounding a gap-clearing Jovian-mass protoplanet.

Effects of forest fragmentation on avian breeding activity.

Biodiversity declines and ecosystem decay follow forest fragmentation; initially, abundant species may become rare or be extirpated. Underlying mechanisms behind delayed extirpation of certain species following forest fragmentation are unknown. Species declines may be attributed to an inadequate number of breeding adults required to replace the population or decreased juvenile survival rate due to reduced recruitment or increased nest predation pressures. We used 10years of avian banding data, 5years before and 4 years after fragment isolation, from the Biological Dynamics of Forest Fragments Project, carried out near Manaus, Brazil, to investigate the breeding activity hypothesis that there is less breeding activity and fewer young after relative to before fragment isolation. We compared the capture rates of active breeding and young birds in 3 forest types (primary forest, fragment before isolation, and fragment after isolation) and the proportion of active breeding and young birds with all birds in each unique fragment type before and after isolation. We grouped all bird species by diet (insectivore or frugivore) and nesting strategy (open cup, cavity, or enclosed) to allow further comparisons among forest types. We found support for the breeding activity hypothesis in insectivorous and frugivorous birds (effect sizes 0.45 and 0.53, respectively) and in birds with open-cup and enclosed nesting strategies (effect sizes 0.56 and 0.44, respectively) such that on average there were more breeding birds in fragments before isolation relative to after isolation. A larger proportion of birds in the community were actively breeding before fragment isolation (72%) than after fragment isolation (11%). Unexpectedly, there was no significant decrease in the number of young birds after fragment isolation, although sample sizes for young were small (n=43). This may have been due to sustained immigration of young birds to fragments after isolation. Together, our results provide some of the strongest evidence to date that avian breeding activity decreases in response to fragment isolation, which could be a fundamental mechanism contributing to ecosystem decay.

Estimation of Surface Downward Longwave Radiation and Cloud Base Height Based on Infrared Multichannel Data of Himawari-8

Surface downward longwave radiation (SDLR) is significant with regard to surface energy budgets and climate research. The uncertainty of cloud base height (CBH) retrieval by remote sensing induces the vast majority of SDLR estimation errors under cloudy conditions; reliable CBH observation and estimation are crucial for determining the cloud radiative effect. This study presents a CBH retrieval methodology built from 10 thermal spectral data from Himawari-8 (H-8) observations, utilizing the random forest (RF) algorithm to fully account for each band’s contribution to CBH. The algorithm utilizes only infrared band data, making it possible to obtain CBH 24 h a day. Considering some factors that can significantly affect the CBH estimation, RF models are trained for different clouds using inputs from multiple H-8 channels together with geolocation information to target CBH derived from CloudSat/CALIPSO combined measurements. The validation results reveal that the new methodology performs well, with a root-mean-square error (RMSE) of only 1.17 km for all clouds. To evaluate the effect of CBH on SDLR estimation, an all-sky SDLR estimation algorithm based on previous CBH predictions is proposed. The new SDLR product not only has a resolution that is noticeably higher than that of benchmark products of the SDLR, such as the Clouds and the Earth’s Radiant Energy System (CERES) and the next-generation reanalysis (ERA5) of the European Centre for Medium-Range Weather Forecasts (ECMWF), but it also has greater accuracy, with an RMSE of 21.8 W m−2 for hourly surface downward longwave irradiance (SDLI).

Monitoring the Biological Aspects of Banded Archer Fish (Toxotes jaculatrix Pallas, 1767) in Bilah River, Labuhanbatu Regency, Indonesia

Banded archer fish (Toxotes jaculatrix Pallas, 1767) is one of the leading commodities of Indonesian ornamental fish. Its production so far is still from the catch of nature. Banded archer fish based on IUCN data has been included in the red list with the Least Concern (LC) category. Currently one of its habitats is found in the Bilah River, Labuhanbatu Regency. The purpose of the study was to information the condition of biological factors related to the relationship of weight length, sex ratio and factors of banded archer fish conditions in the Bilah River. Determination of observation station points by purposive sampling method, where in this study there are 3 stations. Fish sampling in January-March 2022. Fish are caught using splint nets. The entire number of fish caught was measured using a ruler with a length of 30 cm and weight using a digital balance sheet with a precision of 0.01 g. Next the data is tabulated and analyzed using the Microsoft Excel 2013 application. The data of the results of the study is displayed based on each month of observation. The results of the analysis of banded archer fish research data for the long weight relationship from January-March 2022 are negative alometric. The sex ratio of banded archer fish in January 2022 (0.69:1), February 2022 (0.47:1), and March 2022 (0.5:1). The value of the banded archer fish condition factor in January 2022 (1.0304), February 2020 (1.0338), and March 2022 (0.0954). It was concluded that the biological aspects of banded archer fish in the Bilah River still their growth and reproductioan.

A Target-Based Non-Uniformity Self-Correction Method for Infrared Push-Broom Hyperspectral Sensors

Non-uniformity in the response of spectral image elements is an inevitable phenomenon in hyperspectral imaging, which mainly manifests itself as the presence of band noise in the acquired hyperspectral data. This problem is prominent in the infrared band owing to the detector material, operating environment, and other factors. Non-uniformity is an important factor that can affect the quality of the hyperspectral data, which has a serious impact on both data analysis and applications and requires corrections via technical means wherever possible. This paper proposes a novel target-based non-uniformity self-correction method for infrared push-broom hyperspectral images. The Mars Mineralogical Spectrometer (MMS) onboard the Tianwen-1 orbiter was used as the research and application object. The model is constructed and applied to the target scene characteristics and detection patterns of Mars remote sensing exploration, which are combined with the causes of noise generation in the infrared spectral image bands. The design of the MMS dual-channel Visible-Near-Infrared (V-NIR) and Near-Mid-Infrared (N-MIR) co-field of view co-target detection and laboratory calibration data for the V-NIR spectral band can achieve non-uniformity corrections (NUCs). Therefore, for the MMS in-orbit Mars exploration mission, the method selected spectral data (920–1055 nm) characterized by a reduced atmospheric influence to iteratively obtain the homogeneous region, which was used to calculate the non-uniformity correction factor for the N-MIR spectral band. This method was compared, validated, and evaluated with other conventional methods using both laboratory and in-orbit hyperspectral data. The results showed that the experimental data corrections were comparable to laboratory calibrations, with a maximum relative deviation of <2.6%. These results prove that our method not only provides an excellent non-uniformity correction, but also ensures spectral fidelity. It can thus be used as a non-uniformity correction process for the MMS and similar hyperspectral imagers.

Searching hedging instruments against diverse global risks and uncertainties

We evaluate the influence of five major risk and uncertainty factors on four asset classes. Our time-varying findings suggest that each asset hedges only a particular uncertainty factor, whereas gold does more than one factor, especially during COVID-19. Our frequency-based quantile regression (QR) results show that in the raw frequency, gold and Islamic stock can better hedge various uncertainty factors than Bitcoin and crude oil, depending on the market conditions. Additionally, using the frequency bands (e.g., short, medium, and long term) data, we further notice that, depending on the market circumstances and investment horizons, gold and Islamic stock returns are still better hedges for the various risks and uncertainties than Bitcoin and crude oil returns. Our findings have crucial risk and portfolio management implications for investors, portfolio managers, and policymakers.

XMM-Newton Detection of X-Ray Emission from the Metal-polluted White Dwarf G 29-38

A recent analysis of Chandra X-ray data of the metal-polluted white dwarf G 29-38 has revealed X-ray emission that can be attributed to the accretion of debris from a planetary body. In the light of this detection we revisit here archival XMM-Newton observations of G 29-38 from which only an upper limit was derived in the past due to the presence of a relatively bright nearby X-ray source. An analysis of these data in multiple energy bands allows disentangling of the X-ray emission at the location of G 29-38 from that of the nearby source. The similar spectral properties of the source in the XMM-Newton and Chandra observations and their spatial shift, consistent with the proper motion of G 29-38 between these observations, strengthen the origin of the X-ray emission from G 29-38. The X-ray luminosities from both observations are consistent within the 1σ uncertainties, so too are the best-fit plasma temperatures. Although the count number is small, there is tantalizing evidence for line emission in the 0.7–0.8 keV energy band from an optically thin hot plasma. The most likely candidate for this line emission would be the Fe complex at 16 Å.

Peering into the young planetary system AB Pic

Aims. We aim to revisit the formation pathway of AB Pic b, an imaged companion that straddles the exoplanet/brown-dwarf boundary. We based this study on a rich set of observations, which allows us to investigate its orbital and atmospheric properties.Methods. We composed a spectrum of AB Pic b by merging archival medium-resolution (~4000) VLT/SINFONI K band (1.96–2.45 µm) data with published spectra atJandHbands from SINFONI,Lpband from Magellan-AO/CLIO2, and photometric measurements from HST (visible) andSpitzer(mid-infrared). We modeled the spectrum with ForMoSA, following a forward-modeling approach based on two atmospheric models: ExoREM and BT-SETTL13. In parallel, we determined the orbital properties of AB Pic b fitting orbital solutions to astrometric measurements from NaCo (2003 and 2004) and SPHERE (2015).Results. The orbital solutions favor a semi-major axis of 190-50+200au on a highly inclined orbit (edge-on), but with a poorly constrained eccentricity. From the atmospheric modeling with Exo-REM, we derive an effective temperature of 1700 ± 50 K and surface gravity of 4.5 ± 0.3 dex, which are consistent with previous findings, and we report for the first time a c/o ratio of 0.58 ± 0.08, consistent with the value for the Sun. The posteriors are sensitive to the wavelength interval and the family of models used. Given the published rotation period of 2.1 h and our derivedυsin(i) of 73-27+11km s−1, we estimate for the first time the true obliquity of AB Pic b to be between 45 and 135 deg, indicating a rather significant misalignment between the spin and orbit orientations of the planet. Finally, the existence of a proper-motion anomaly between the HIPPARCOSandGaiaEarly Data Release 3 compared to our SPHERE detection limits and adapted radial velocity limits indicates the potential existence of a ~6MJupinner planet orbiting from 2 to 10 au (40–200 mas).Conclusions. The possible existence of an inner companion and the likely misalignment of the spin-axis orientation strongly favor a formation path by gravitational instability or core accretion within a protoplanetary disk at a smaller orbital radius followed by a dynamical interaction which scattered AB Pic b to its current location. Confirmation and characterization of this unseen inner exoplanet and access to a broader wavelength coverage and higher spectral resolution for the characterization of AB Picb will be essential for probing the uncertainties associated with the atmospheric and orbital parameters.

Six decades of North American bird banding records reveal plasticity in migration phenology.

The timing of avian migration has evolved to exploit critical seasonal resources, yet plasticity within phenological responses may allow adjustments to interannual resource phenology. The diversity of migratory species and changes in underlying resources in response to climate change make it challenging to generalize these relationships. We use bird banding records during spring and fall migration from across North America to examine macroscale phenological responses to interannual fluctuations in temperature and long-term annual trends in phenology. In total, we examine 19 species of North American wood warblers (family Parulidae), summarizing migration timing from 2,826,588 banded birds from 1961 to 2018 across 46 sites during spring and 124 sites during fall. During spring, warmer spring temperatures at banding locations translated to earlier median passage dates for 16 of 19 species, with an average 0.65-day advancement in median passage for every 1°C increase in temperature, ranging from 0.25 to 1.26 days °C-1 . During the fall, relationships were considerably weaker, with only 3 of 19 species showing a relationship with temperature. In those three cases, later departure dates were associated with warmer fall periods. Projecting these trends forward under climate scenarios of temperature change, we forecast continued spring advancements under shared socioeconomic pathways from 2041 to 2060 and 2081 to 2100 and more muted and variable shifts for fall. These results demonstrate the capacity of long-distance migrants to respond to interannual fluctuations in temperatures, at least during the spring, and showcase the potential of North American bird banding data understanding phenological trends across a wide diversity of avian species.

Spatio-temporal analysis of urban heat island (UHI) and its effect on urban ecology: The case of Mekelle city, Northern Ethiopia

The bio-geophysical effects of land cover classes are considered to be an important factor in land surface temperature variations between urban and suburban areas. This means that major cities are significantly warmer than surrounding suburban or rural areas, which is known as the Urban Heat Island (UHI) effect. The aim of this study was to assess and analyze the spatiotemporal variation, correlation and impact of UHIs on Mekelle city (1990–2020) using remote sensing techniques. The study's primary objective was accomplished using the a number of techniques, including the extraction of LULC classes, estimation of the seasonal LSTs, assessment of UHI and UTFVI, and showing the relationship between LULC and LST as well as the interactions between UHI, UTFVI, and urban LULC classes. By analyzing TIRs/OLI thermal band data after calibrating uncertainty in the images and validating it using the concept of theoretical relationships and least squares fitting method, estimates of local LST, UHI (both in Mekelle and periphery rural areas), and UTFVI were obtained. The result of standard multiple regression models showed that impervious urban land surface, built-up areas, and dry bare soil highly contribute and influence variation at LST intensity caused for the formation of UHI in the study area. The result showed that the maximum UHI value in Mekelle city was 2.73 °C during the dry season in 1990. It decreased slightly to 2.53 °C in 2000 and then increased regularly to 2.83 °C and 2.98 °C in 2010 and 2020, respectively. To determine the city's UHI status in comparison to eight selected peripheral suburban areas, a trend analysis has been done. The UHI intensity of Mekelle city was higher relatively to that of most of the periphery suburban districts, particularly in 2020 (both dry and rainy seasons); this could be due to the city's explosive growth. It's worth noting that the research area affected by the urban heat island effect has grown over time, and as a result, the study area also has severe microclimate conditions that primarily damage the quality of urban life and create the worst conditions for thermal discomfort. The results of this study provide major conceptual understandings of how improper distribution and use of urban land affects the urban environment and fuels the creation of the UHI and thermal discomfort phenomena. Hence policy makers and urban planners should consider the effects of LST and UHI and integrate UHI comprehensive mitigation strategies with urban development patterns, and current and projected local climate changes in order to create sustainable urban environments, cities, and communities. In conclusion, compared to the conventional method, satellite remote sensing provides a faster and more efficient method for researching LST and UHI.

A theoretical study of propynal under interstellar conditions and beyond, covering low-frequency infrared spectra, spectroscopic constants, and hot bands

ABSTRACTFrom an astrochemical point of view, propynal is a complex organic molecule. Moreover, it is a potential candidate for the formation of prebiotic propanal and propenal. Therefore, this molecule is of particular interest for astrochemical investigations. As it has been detected in the interstellar medium, it is of high relevance in this field of research. Although experimental data are available for the vibrational fundamental bands and rotational constants, experimental data for vibrational overtones and combination bands are scarce and fairly old. Additionally, high-level ab initio calculations are also not reported. In this work, we provide accurate quantum chemical calculations as well as a detailed analysis of vibrational and rovibrational properties for this molecule. The low-frequency spectrum up to $350\, \,\,\text{cm}^{-1}$ has been studied for temperatures between 10 and $300\, \text{K}$. For the same temperature range, partition functions are provided. Furthermore, the impact of hot bands up to room temperature has been investigated. A comparison of our results with experimental data is provided for the rotational constants, geometrical parameters, and a rovibrational spectrum. The underlying potential energy surface within these calculations is based on explicitly correlated coupled-cluster theory and includes up to four-mode coupling terms within an n-mode expansion. The vibrational and rovibrational calculations rely on vibrational and rovibrational configuration interaction theories, respectively.

A Review of the Far-Reaching Usage of Low-Light Nighttime Data

To assess the current and future utility of low-light satellite data, this paper reviewed 1630 papers, presentations, theses, and dissertations using day/night band (DNB) data from the visible infrared imaging radiometer suite (VIIRS) imager and its precursor, the Defense Meteorological Satellite Program’s Operational Linescan system (DMSP-OLS) series from the 1970s through to the year 2021. By the way of a categorical system, we take inventory of the myriad applications of these data to a wide variety of disciplines, ranging from social to natural science, oceans to atmosphere, and biology to civil engineering. Papers from social science fields dominate this spectrum, pointing to the unique aspect of low-light observations in their ability to observe aspects of human civilization at night. We also look at the stratification between applications using natural vs. artificial light, the use of moonlight, and the context of the key earth climate system elements. In light of these findings, a discussion is provided for the future of low-light measurements. Since the start of the VIIRS series, there has been a rapid increase in interest in the use of these data for numerous fields, pointing towards a nascent field centered on the nocturnal earth system science that is enabled by these novel and newly quantifiable measurements. This study is of significant importance in evaluating current uses of low-light data and possible architecture solutions for next-generation satellites.