Source Distribution Research Articles

Analisis Indeks Kinerja Jaringan Saluran Irigasi Studi Kasus Irigasi Sungai Siulak Deras Kabupaten Kerinci

The Siulak Deras Irrigation canal network is the main source of water distribution in several areas. The Siulak Deras Irrigation Area in Kerinci Regency was built in 1993 with water flow regulation and complete Siulak Deras Irrigation network facilities including the Technical irrigation group. As time goes by, the service function of the irrigation network experiences a decline in performance, so that operation and maintenance of the irrigation network is an activity that is really needed to make the irrigation network remain optimal and maintain its performance. In this regard, an assessment of the Performance Index of the irrigation system in the Siulak Deras Irrigation Area needs to be carried out to determine the value of aspects or components that are not yet optimal in this irrigation area so that special treatment can be immediately carried out or improved. The assessment is carried out by starting an inventory of the condition and function of the physical infrastructure of the irrigation network, planting productivity results, supporting facilities, organization, documentation and institutional P3A to determine the performance value and recommendations for handling it. The results of the irrigation network system performance index in the Siulak Deras irrigation area for the Left Irrigation Channel section are 69.59%, which means that the irrigation system performance is close to good performance, namely 70%, but still needs to be improved and needs attention. With a physical infrastructure value of 29.32%, planting productivity 14.21%, supporting facilities 6.15%, personnel organization 10.94%, documentation 2.46%, and P3A institutions 6.50%. Based on the performance index value obtained, and the total performance score in the form of a percentage, it is necessary to specifically maintain and repair physical facilities and infrastructure at several points that are damaged, facilitate supporting facilities for OP officers so that officers can act quickly and precisely in carrying out activities, and paying more attention to P3A so that it can always contribute to maintaining and utilizing the Siulak Deras irrigation network properly and optimally in the future.

Distributed Software Build Assurance for Software Supply Chain Integrity

Computing and networking are increasingly implemented in software. We design and build a software build assurance scheme detecting if there have been injections or modifications in the various steps in the software supply chain, including the source code, compiling, and distribution. Building on the reproducible build and software bill of materials (SBOM), our work is distinguished from previous research in assuring multiple software artifacts across the software supply chain. Reproducible build, in particular, enables our scheme, as our scheme requires the software materials/artifacts to be consistent across machines with the same operating system/specifications. Furthermore, we use blockchain to deliver the proof reference, which enables our scheme to be distributed so that the assurance beneficiary and verifier are the same, i.e., the node downloading the software verifies its own materials, artifacts, and outputs. Blockchain also significantly improves the assurance efficiency. We first describe and explain our scheme using abstraction and then implement our scheme to assure Ethereum as the target software to provide concrete proof-of-concept implementation, validation, and experimental analyses. Our scheme enables more significant performance gains than relying on a centralized server thanks to the use of blockchain (e.g., two to three orders of magnitude quicker in verification) and adds small overheads (e.g., generating and verifying proof have an overhead of approximately one second, which is two orders of magnitude smaller than the software download or build processes).

Clean and Complete Protein Digestion with an Autolysis Resistant Trypsin for Peptide Mapping.

Peptide mapping requires cleavage of proteins in a predictable fashion so that target protein-specific peptides can be reliably identified and quantified. Trypsin, a commonly used protease in this process, can also undergo self-cleavage or autolysis, thereby reducing the effectivity and even cleavage specificity at lysine and arginine residues. Here, we report highly efficient and reproducible peptide mapping of biotherapeutic monoclonal antibodies. We highlight the properties of a homogeneous chemically modified trypsin on thermal stability, a 54% increase in melting temperature with an 84% increase in energy required for unfolding, an indication of more thermally stable trypsin, >90% retained intact mass peak area after exposure to digestion conditions confirming autolysis resistance, 10× more intensity for intact enzyme compared to trypsin of similar source and narrower molecular weight distribution with LC-MS indicative of low degradation compared to 3 other types of trypsin. Finally, we show the utility of this autolysis-resistant trypsin in characterizing biotherapeutic monoclonal antibodies consistently and reliably showing a >30% reduction in missed cleavage for a short-duration protein digestion time of 30 min compared to heterogeneously modified trypsin of a similar source.

Ensemble width estimation in HRTF-convolved binaural music recordings using an auditory model and a gradient-boosted decision trees regressor

Binaural audio recordings become increasingly popular in multimedia repositories, posing new challenges in indexing, searching, and retrieval of such excerpts in terms of their spatial audio scene characteristics. This paper presents a new method for the automatic estimation of one of the most important spatial attributes of binaural recordings of music, namely “ensemble width.” The method has been developed using a repository of 23,040 binaural excerpts synthesized by convolving 192 multi-track music recordings with 30 sets of head-related transfer functions (HRTF). The synthesized excerpts represented various spatial distributions of music sound sources along a frontal semicircle in the horizontal plane. A binaural auditory model was exploited to derive the standard binaural cues from the synthesized excerpts, yielding a dataset representing interaural level and time differences, complemented by interaural cross-correlation coefficients. Subsequently, a regression method, based on gradient-boosted decision trees, was applied to the formerly calculated dataset to estimate ensemble width values. According to the obtained results, the mean absolute error of the ensemble width estimation averaged across experimental conditions amounts to 6.63° (SD 0.12°). The accuracy of the method is the highest for the recordings with ensembles narrower than 30°, yielding the mean absolute error ranging between 0.8° and 10.2°. The performance of the proposed algorithm is relatively uniform regardless of the horizontal position of an ensemble. However, its accuracy deteriorates for wider ensembles, with the error reaching 25.2° for the music ensembles spanning 90°. The developed method exhibits satisfactory generalization properties when evaluated both under music-independent and HRTF-independent conditions. The proposed method outperforms the technique based on “spatiograms” recently introduced in the literature.

Defending Cloud Web Applications using Machine Learning-Driven Triple Validation of IP Reputation by Integrating Security Operation Center

This paper will present an innovative system method of IPR (IP Address Reputation) validation with the assistance of clause of (ML) machine learning for discovering malicious IPs, while also viewing the importance of security of installed applications on AWS (Amazon Web Services) servers. The ML, SANS and Abuse DB datasets that were verified are being integrated through the Wazuh Security Operation Centre (SOC) stage to consume issues at the log ingesting IP address-related level. Having integrated extraction of IPs Wazuh agents, the output does match MITRE ATT&CK framework-filtered IP address from the Wazuh SOC. These algorithms and models based on natural language processing will flag suspicious patterns across IPs through the process of machine learning and prevent the event of a cyber attack at the time. This integration not only boosts cybersecurity information through a single point source of distribution, but it also provides security finds and other resources to prove and maintain awareness against malicious IPs. The final solution includes using the maximum amounts of bad IPs blocking in the ‘IP-List’ of AWS WAF and, if they are added to the Blacklist automatically, checking them through an automatic ML-based signature validation process.

A multiple x-ray-source array (MXA) system with a planar two-dimensional source distribution for digital breast tomosynthesis.

Digital breast tomosynthesis (DBT) has outpaced digital mammography in clinical adoption in the United States; however, substantial technological limitations remain to image quality in DBT, including undersampling from a one-dimensional (1D) scan geometry, x-ray source motion during acquisition, and patient motion artifacts from long exam times. A thermionic cathode x-ray system employing two-dimensional (2D, planar) multiple x-ray-source arrays (MXA) is proposed to improve DBT image quality. A 1D MXA, consisting of a linear array of thermionic cathodes was used to simulate a 2D MXA. The 1D MXA included 11 focal spots separated by a distance of =23mm. The 11 cathodes were paired with 11 molybdenum 50mm diameter anode disks, mounted on a rotating shaft within a single vacuum enclosure. Image quality was investigated as a function of MXA configuration by integrating the 1D MXA with a 200 × 250 mm2 flat panel detector at a source-to-detector distance of 630mm, resulting in a 20° tomographic arc. To simulate a 2D MXA, the detector (with phantom) was translated orthogonally to the linear array by a distance ( ) ranging from =0mm (conventional 1D) to =57mm. All sources operated at 30kV with 80mA and 4.5 mAs/pulse, yielding ∼100 mAs per DBT dataset. DBT reconstructions involved 22 projections and used filtered backprojection with a ramp and Hann apodization filter. Volumetric reconstructions for each source were weighted by sampling differences between sources, and averaged. Image quality was assessed in terms of contrast-to-noise ratio (CNR), background clutter noise and power spectrum, and slice sensitivity profile (SSP) using a set of physical phantoms, including: (i) contrast-detail signals coupled to spherical clutter (PMMA in air); (ii) an SSP phantom; (iii) a commercial "breast" phantom (CIRS BR3D, Sun Nuclear, Norfolk, VA); and (iv) bovine muscle. Background clutter noise amplitude reduced monotonically from the 1D MXA (σclutter=5.9 A.U., =0mm) and 2D MXA arrays with increasing , with statistical significance between the 1D MXA and 2D MXA with =57mm (σclutter=5.0 A.U., p<0.001). The contrast-detail/clutter phantom demonstrated CNR from the 2D MXA (δ=57mm) outperforming the 1D MXA in all combinations of contrast and detail. 2D power spectrum analysis of clutter demonstrated a pronounced Fourier domain null cone for the 1D MXA in the anterior field-of-view (away from the 1D MXA position), whereas the 2D MXA geometry (δ=57mm) did not exhibit the null cone. The SSP was 15%-50% narrower (FWHM) for the 2D versus the 1D geometry, across all reconstruction setups. The advantages of a 2D source geometry for DBT imaging were demonstrated quantitatively compared to a conventional 1D line of x-ray sources. The improvement in the 2D geometry was attributed both to improved Fourier domain sampling and reduced SSP. We conclude that 2D MXA sources have the potential to substantially improve DBT imaging in comparison to existing commercial DBT systems.

Gradient descent optimization of acoustic holograms for transcranial focused ultrasound

Acoustic holographic lenses, also known as acoustic holograms, can change the phase of a transmitted wavefront in order to shape and construct complex ultrasound pressure fields, often for focusing the acoustic energy on a target region. These lenses have been proposed for transcranial focused ultrasound (tFUS) to create diffraction-limited focal zones that target specific brain regions while compensating for skull aberration. Holograms are currently designed using time-reversal approaches in full-wave time-domain numerical simulations. Such simulations need time-consuming computations, which severely limits the adoption of iterative optimization strategies. In the time-reversal method, the number and distribution of virtual sources can significantly influence the final sound field. Because of the computational constraints, predicting these effects and determining the optimal arrangement is challenging. This study introduces an efficient method for designing acoustic holograms using a volumetric holographic technique to generate focused fields inside the skull. The proposed method combines a modified mixed-domain method for ultrasonic propagation with a gradient descent iterative optimization algorithm. The findings are further validated in underwater experiments with a realistic 3D-printed skull phantom. This approach enables substantially faster holographic computation than previously reported techniques. The iterative process uses explicitly defined loss functions to bias the ultrasound field’s optimization parameters to specific desired characteristics, such as axial resolution, transversal resolution, coverage, and focal region uniformity, while eliminating the uncertainty associated with virtual sources in time-reversal techniques. The proposed techniques enable more rapid hologram computation and more flexibility in tailoring ultrasound fields for specific therapeutic requirements.

Construction of Cross-basin Ecological Security Patterns Based on Carbon Sinks and Landscape Connectivity

Artificial water system creation and land use changes have great effects on ecosystems. The construction of cross-basin ecological security patterns based on carbon sinks and landscape connectivity plays a key role in regional ecological environment protection. The linkage area between the Xiang River and the Li River was selected as the research object. Based on the land use data from 2000 to 2020, this study examined the ecological security network of the Xiang-Li connected region using the InVEST model combined with morphological spatial pattern analysis （MSPA） and evaluated the temporal and spatial evolution of carbon storage and ecological security patterns. The results showed that： ① From 2000 to 2020, the land cover types of the Xiang-Li linkage area were mainly forest land and arable land. The changes of land use types were characterized by decreases in arable land, forest land, and grassland and by increases in watersheds and construction land. ② The carbon storage in the Xiang-Li linkage area was characterized by a blocky distribution, and the high and medium areas were dominant. The carbon stock increased slowly from 2000 to 2010 and decreased dramatically from 2010 to 2020, with a cumulative decrease of 18.32×103 t due to the influence of land use changes. ③ The area of ecological sources （five in total） decreased firstly and then increased, whereas the length of ecological corridors （ten in total） increased firstly and then decreased in the Xiang-Li linkage area. Overall, in the process of urbanization, the distribution of the high ecological resistance value in the Xiang-Li linkage area gradually shifted to the northeast with an expansion, whereas the barycenter of the ecological safety pattern shifted to the southwest. Determining the dynamic distribution and stability of ecological sources by coupling carbon storage patches and landscape patterns can provide a new way to construct ecological security patterns in cross-basin ecosystems.

Phase volume correlation approach for overcoming decorrelation in three-dimensional phase-sensitive optical coherence elastography.

The dynamic measurement range in phase-sensitive optical coherence elastography (PhS-OCE) is limited for the phase decorrelation induced by pixel-level displacements in precision measurement, where the consideration of the time-resolved incremental method and in-plane pixels tracking method is insufficient to recover the phase holistically. This work presented a phase volume correlation (PVC) approach to handle the phase decorrelation in three-dimensional PhS-OCE. By utilizing the ability of the discontinuous source diagram to quantify voxel phase correlation levels, the PVC establishes a wrapped phase-matching equation aimed at optimizing the number of volumetric source distributions. The three-dimensional pixel-level motions in the deformed phase space can be evaluated by solving the optimization model for phase matching, thereby enabling the reconstruction of the volumetric phase variation corrupted by decorrelation. The large deformations experiments including diffident loadings, i.e., stretching, three-point bending, and light-cured, verified the proposed PPVC approach's of feasibility, reliability, and stability. The contribution of this work can dramatically enhance the dynamic measuring range in three-dimensional PhS-OCE.

Deep Learning with Crested Porcupine Optimizer for Detection and Classification of Paddy Leaf Diseases for Sustainable Agriculture

India has a vast number of inhabitants and the main food source distribution is from agriculture. Agricultural lands are being demolished generally owing to plant and crop illnesses. The detection of plant diseases by using image processing models can aid agriculturalists in defending the farming area from damaging or affecting it. Paddy is the main harvest worldwide. Early recognition of the paddy diseases at dissimilar phases of development is very vital in paddy production. However, the present manual technique in identifying and classifying paddy diseases needs a very educated farmer and is time-consuming. Deep learning (DL) is an effectual research area in the classification of agriculture patterns where it can efficiently solve the problems of diseases identification. Therefore, the articles focus on the design and expansion of Deep Learning based Crested Porcupine Optimizer for the Detection and Classification of Paddy Leaf Diseases (DLCPO-DCPLD) method for Sustainable Agriculture. The main aim of the DLCPO-DCPLD method use DL method for the recognition and identification of rice plant leaf diseases. To accomplish this, the DLCPO-DCPLD technique performs the image pre-processing using Median Filtering (MF) to recover the excellence of the input frames. Next, the ConvNeXt-L method is applied for extraction of feature vectors from the pre-processed images. Also, the Conditional Variational Autoencoder (CVAE) model is utilized for the automated classification of Paddy Leaf diseases. Eventually, the hyperparameter tuning of the CVAE technique is accomplished by implementing the Crested Porcupine Optimizer (CPO) technique. To safeguard the enhanced predictive results of the DLCPO-DCPLD method, a sequence of experimentations is implemented on the benchmark dataset. The experimental validation of the DLCPO-DCPLD method portrayed a superior accuracy value of 99.12% over existing approaches.

Localization of correlated moving sources from numerical flyover scenarios

The noise emissions during flyover test can be analyzed with a phased microphone array placed on the ground. If the flight path is known, an acoustic imaging technique is then used to map the sound pressure over the fuselage, in order to locate the most emergent aeroacoustic sources. Most of the dedicated array techniques assume a spatial distribution of uncorrelated equivalent sources to be sought. This paper deals with the localization of correlated moving sources, and demonstrates the effectiveness of an uncorrelated-based deconvolution technique for this purpose. The technique is applied on synthetic data from three different numerical scenarios, including a simplistic configuration of an overflying aircraft, which involve a small number of sparse, correlated monopoles in motion.

Sound Source Localization at the Rolling Tire

A vehicle's tire rolling on pavement causes complex noise generating mechanisms which are a dominant sub-source of vehicle traffic noise. For the quantification of noise levels due to these tire-pavement interactions, standardized acoustic measurement methods exist, such as the Close-Proximity (CPX) method. These methods require microphones mounted near a rolling tire. However, they do not provide insight in the distribution and amplitudes of present sound sources. In this contribution, we aim at identifying the dominant sound sources of the rolling tire at discrete frequencies. This is accomplished by an inverse method combining microphone array measurements and Finite Element (FE) simulations. The microphone measurements were recorded on Austrian highways with a large measurement trailer built for research purposes. With the considered inverse method, not only the amplitudes of the dominant sound sources but also their phases can be identified. With these identified sources and a validated FE model of the measurement trailer, the sound pressure field within the trailer may be computed. The thereby calculated sound pressure is compared to measurements at the CPX microphone positions and other locations within the trailer.

An investigation of the performances of asynchronous measurements methods

In acoustical imaging and source localization, several methods for the exploitation of asynchronous array measurements have been proposed, based on covariance matrix completion, Bayesian estimation, fusion of beamforming maps, etc. The goal of asynchronous measurements is to reach the performance of large and dense arrays, with reduced experimental effort, by moving an array of limited size between experiments, and fuse the obtained data to produce an estimation of the distribution of acoustical sources. In this study, we consider the performances that one can expect from asynchronous measurements, and investigate the actual performances of several methods from the state of the art. In particular, the mean squared errors of the estimation of the position and power of an acoustical source are estimated using simulations and experimental measurements.

Constraining the sources of archaeal tetraether lipids in multiple cold seep provinces of the Cascadia Margin

Archaeal isoprenoid glycerol dialkyl glycerol tetraether lipid (iGDGT) abundance profiles and carbon isotopic compositions reflect the relative distributions of archaeal sources, including planktonic, benthic, and methane-cycling contributions. Here, we analyze the carbon isotope ratios of iGDGTs purified from sediments of three different cold seep sites in Cascadia Margin, off the coast of Washington, USA. Together with relative abundance and glycerol configurations, we use the carbon isotope ratios to estimate the contributions of multiple archaeal sources to the sedimentary iGDGT assemblages and their impact on values of the TEX86 and methane indices. Using a Bayesian mixing model, we robustly characterize three potential endmembers by determining their characteristic lipid distributions, inferred contributions to the total sediment inventory, and carbon isotopic signatures. Despite the geographic proximity of the sample locations, we find site-specific heterogeneity in relative iGDGT abundances and δ13C values. Planktonic and benthic methane-cycling sources predominate in all cases (contributing > 98% of iGDGTs), while benthic non-methane cycling archaea contribute minimally to the sedimentary lipid pool. Environments with higher methane influence show an increased presence of anti-parallel iGDGTs, indicating that methane-cycling archaea may dominantly or exclusively synthesize iGDGTs in this configuration. Our results quantify the relationship between the methane index (MI) and methane impact in systems dominated by planktonic and benthic methane-cycling archaea. Within the framework of the TEX86 temperature proxy, this permits a quantitative demonstration that it is overly simplistic to apply a MI cutoff threshold as a binary indicator to determine methane influence, and caution is needed when taking this approach in paleoclimate reconstructions.

Evaluating monsoon season heavy metal contamination in groundwater of Uttar Dinajpur District using pollution indices and Principal Component analysis

Heavy metal contamination in groundwater poses significant environmental and public health challenges globally. This study investigates the presence and distribution of heavy metal contamination in groundwater within Uttar Dinajpur District, India, focusing on zinc (Zn), manganese (Mn), copper (Cu), iron (Fe), and lead (Pb). Groundwater samples were collected and analysed for heavy metal concentrations, and statistical analyses, including descriptive statistics, histograms, box plots, Heavy Metal Pollution Index (HPI) analysis, Metal Index (MI) analysis, and Principal Component Analysis (PCA), were conducted to assess contamination levels, spatial distribution, and potential sources. Results indicate varying levels of heavy metal contamination across the district, with manganese, iron, and lead frequently exceeding permissible limits, posing potential health risks. The PCA revealed common sources and relationships among heavy metals, aiding in understanding contamination patterns. The study underscores the importance of continuous monitoring and targeted interventions to manage heavy metal contamination in groundwater, emphasizing the need for further research to develop effective mitigation strategies.

Effect of discretization on off-axis listeners in binaural synthesis based on the optimal source distribution

3D sound reproduction based on binaural synthesis over loudspeakers is one of the most important techniques in immersive spatial audio. The system inversion involved in the binaural synthesis gives rise to several problems such as dynamic range loss, distortion of the reproduction transfer function, and a lack of robustness to room reflections. The principle of Optimal Source Distribution was proposed to overcome these problems. This utilizes a pair of conceptual monopole transducers whose azimuthal location varies continuously as a function of frequency. Another potential of the principle is that it can provide listening positions for multiple off-axis listeners, in addition to the on-axis listener whose inverse matrix is given, since the phase interference state is aligned at all frequencies. However, these continuously varying transducers are too conceptual to realize in practice. It is necessary to provide discretized sound sources with appropriate frequency ranges according to the principle. In this paper, the effect of discretization of the principle on off-axis listeners' performance is numerically investigated to reveal how those off-axis sweet spots are formed as the discretization becomes finer. An experiment is also carried out using an actual discretized system and the effect of the head-related transfer functions is considered.

Estimation of the surface impedance based on the discrete complex image source method and array processing

This paper introduces a method for estimating the sound absorption coefficient of a large panel of absorbing material from measurements using a microphone array. The method relies upon describing the pressure field as the set of monopoles obtained when the reflection coefficient is represented as the Laplace Transform of an image source distribution. This formulation results in a well-behaved integral equation, which is discretized using Gaussian quadrature to yield an inverse problem. It is possible to determine the regularized solution to the inverse problem and reconstruct the surface impedance of the sample by conducting measurements of the sound pressure at multiple points of observation. This sound field representation is adequate for sound sources in the close range of the material and the array. The paper presents simulated and experimental investigations to delineate the efficacy of the proposed measurement technique against the well-known image source method, thereby highlighting its significant potential to advance acoustic characterization methodologies.

Radiation image reconstruction and uncertainty quantification using a Gaussian process prior

We propose a complete framework for Bayesian image reconstruction and uncertainty quantification based on a Gaussian process prior (GPP) to overcome limitations of maximum likelihood expectation maximization (ML-EM) image reconstruction algorithm. The prior distribution is constructed with a zero-mean Gaussian process (GP) with a choice of a covariance function, and a link function is used to map the Gaussian process to an image. Unlike many other maximum a posteriori approaches, our method offers highly interpretable hyperparamters that are selected automatically with the empirical Bayes method. Furthermore, the GP covariance function can be modified to incorporate a priori structural priors, enabling multi-modality imaging or contextual data fusion. Lastly, we illustrate that our approach lends itself to Bayesian uncertainty quantification techniques, such as the preconditioned Crank–Nicolson method and the Laplace approximation. The proposed framework is general and can be employed in most radiation image reconstruction problems, and we demonstrate it with simulated free-moving single detector radiation source imaging scenarios. We compare the reconstruction results from GPP and ML-EM, and show that the proposed method can significantly improve the image quality over ML-EM, all the while providing greater understanding of the source distribution via the uncertainty quantification capability. Furthermore, significant improvement of the image quality by incorporating a structural prior is illustrated.

Tourism-generated energy use characteristics and sustainability transitions

Mountain tourism destinations are characterized as significantly impacted by their remoteness, seasonal climatic variations, and fragile ecosystems. These factors greatly influence the development, distribution, and consumption of energy sources in the tourism sector. With the increasing popularity of mountain parks and protected areas as tourism destinations, it is critical to understand the interplay between tourism development (e.g. expansion of tourism facilities), patterns of energy sources (diversity and consumption levels) in the tourism sector, and overall sustainability of resources use at the destination level. Current literature reveals a dearth of research on energy issues in mountain protected areas, which is somewhat surprising as energy and resource consumption issues are becoming more important from a climate change perspective. This paper examines contemporary energy use patterns in the Sagarmatha (Mount Everest) National Park of Nepal. A mixed methods is applied to analyze development trends and transitions in energy use. Data were collected based on questionnaire surveys of tourism facilities, complemented by semi-structured interviews. The findings indicate that energy sources used in the facilities have gone through a significant change, from a firewood-dependent tourism center to increasing use of alternative sources including liquified petroleum gas (LPG) and hydroelectricity. About one-third of the facilities did not use firewood. The heterogeneous geographic distribution of the facilities affects the spatial use of energy sources. Furthermore, we argue that the coordination efforts between the national park administration and local communities, the growth of tourism, the construction of hydropower plants, and the advancement of transportation are the leading causes of the changes in energy use patterns. Knowledge of the energy use characteristics and drivers influencing the energy transitions can inform future policies that promote a low-carbon economy in mountain destinations.

Distribution characteristics and sources of ultraviolet absorbents in facility agricultural soils in China

The environmental contamination of ultraviolet absorbents (UVAs) has attracted global attention for the persistence, bioaccumulation and ecotoxicity. However, little is known about the content and distribution characteristics of UVAs in agricultural soils, especially in facility agricultural soils. In this study, the contents and distribution characteristics of 16 UVAs were surveyed in agricultural facility soils (N = 61) and field soil samples (N = 61) from 27 provinces in China. The total content of 16 UVAs (Σ16UVAs) in facility soils (mean 64.2 ± 55.4 ng/g) was higher than that in field soils (mean 9.66 ± 7.66 ng/g), suggesting that UVAs in facility soils are associated with mulch film. The Σ16UVAs content in the soil mulched with biodegradable (PBAT) film was higher than that in the soil mulched with polyethylene (PE) film, which indicated that the UVA pollution in the soil mulched with biodegradable film was more serious. With the continuous promotion of the use of biodegradable films may pose a threat to soil and ecological health. Therefore, studies on the content and distribution characteristics of UVAs in facility soils are needed to provide scientific basis for the controlling and monitoring of novel pollutants.