Research status and prospect of purification technology of sulfur-containing odor gas.

Catalytic purification of sulphur-containing malodorous gases has attracted wide attention because of its advantages of high purification efficiency, low energy consumption and lack of secondary pollution. The selection of efficient catalysts is the key to the problem, while the preparation and optimisation of catalysts depend on the analysis of experimental results and in-depth mechanistic analysis. By analysing the published literature, bibliometric analysis can identify existing research hotspots, the areas of interest and predict development trends, which can help to identify hot catalysts in the catalytic purification of sulphur-containing odours and to investigate their catalytic purification mechanisms. Therefore, this paper uses bibliometric analysis, based on Web Of Science and CNKI databases, CiteSpace and VOS viewer software to collate and analyse the literature on the purification of sulphur-containing odour pollutants, to identify the current research hotspots, to summarise the progress of research on the catalytic purification of different types of sulphur-containing odours, and to analyse their reaction mechanisms and kinetics. On this basis, the research progress of catalytic purification of different kinds of sulfur odour is summarized, and the reaction mechanism and dynamics are summarized.

Enhancing ozone catalytic decomposition through acid treatment of α-MnO2 for improved activity and humidity resistance.

Post-etching method using dilute acid solutions is an effective technology to modulate the surface compositions of metal-oxide catalysts. Here the α-MnO2 catalyst treated with 0.1 mol/L nitric acid exhibits higher ozone decomposition activity at high relative humidity than the counterpart treated with acetic acid. Besides the increases in surface area and lattice dislocation, the improved activity can be due to relatively higher Mn valence on the surface and newly-formed Brønsted acid sites adjacent to oxygen vacancies. The remnant nitro species deposited on the catalyst by nitric acid treatment is ideal hydrophobic groups at ambient conditions. The decomposition route is also proposed based on the DRIFTS and DFT calculations: ozone is facile to adsorb on the oxygen vacancy, and the protonic H of Brønsted acid sites bonds to the terminal oxygen of ozone to accelerate its cleavage to O2, reducing the reaction energy barrier of O2 desorption.

A new method for the rapid identification of external water types in rainwater pipeline networks using UV-Vis absorption spectroscopy.

Ultraviolet-visible (UV-Vis) absorption spectroscopy, due to its high sensitivity and capability for real-time online monitoring, is one of the most promising tools for the rapid identification of external water in rainwater pipe networks. However, difficulties in obtaining actual samples lead to insufficient real samples, and the complex composition of wastewater can affect the accurate traceability analysis of external water in rainwater pipe networks. In this study, a new method for identifying external water in rainwater pipe networks with a small number of samples is proposed. In this method, the Generative Adversarial Network (GAN) algorithm was initially used to generate spectral data from the absorption spectra of water samples; subsequently, the multiplicative scatter correction (MSC) algorithm was applied to process the UV-Vis absorption spectra of different types of water samples; following this, the Variational Mode Decomposition (VMD) algorithm was employed to decompose and recombine the spectra after MSC; and finally, the long short-term memory (LSTM) algorithm was used to establish the identification model between the recombined spectra and the water source types, and to determine the optimal number of decomposed spectra K. The research results show that when the number of decomposed spectra K is 5, the identification accuracy for different sources of domestic sewage, surface water, and industrial wastewater is the highest, with an overall accuracy of 98.81%. Additionally, the performance of this method was validated by mixed water samples (combinations of rainwater and domestic sewage, rainwater and surface water, and rainwater and industrial wastewater). The results indicate that the accuracy of the proposed method in identifying the source of external water in rainwater reaches 98.99%, with detection time within 10 s. Therefore, the proposed method can become a potential approach for rapid identification and traceability analysis of external water in rainwater pipe networks.

Impact of residual antibiotics on microbial decomposition of livestock manures in Eutric Regosol: Implications for sustainable nutrient recycling and soil carbon sequestration.

The land application of livestock manure has been widely acknowledged as a beneficial approach for nutrient recycling and environmental protection. However, the impact of residual antibiotics, a common contaminant of manure, on the degradation of organic compounds and nutrient release in Eutric Regosol is not well understood. Here, we studied, how oxytetracycline (OTC) and ciprofloxacin (CIP) affect the decomposition, microbial community structure, extracellular enzyme activities and nutrient release from cattle and pig manure using litterbag incubation experiments. Results showed that OTC and CIP greatly inhibited livestock manure decomposition, causing a decreased rate of carbon (28%-87%), nitrogen (15%-44%) and phosphorus (26%-43%) release. The relative abundance of gram-negative (G-) bacteria was reduced by 4.0%-13% while fungi increased by 7.0%-71% during a 28-day incubation period. Co-occurrence network analysis showed that antibiotic exposure disrupted microbial interactions, particularly among G- bacteria, G+ bacteria, and actinomycetes. These changes in microbial community structure and function resulted in decreased activity of urease, ß-1,4-N-acetyl-glucosaminidase, alkaline protease, chitinase, and catalase, causing reduced decomposition and nutrient release in cattle and pig manures. These findings advance our understanding of decomposition and nutrient recycling from manure-contaminated antibiotics, which will help facilitate sustainable agricultural production and soil carbon sequestration.

Co-doped cryptomelane-type manganese oxide in situ grown on a nickel foam substrate for high humidity ozone decomposition.

Monolithic catalysts with excellent O3 catalytic decomposition performance were prepared by in situ loading of Co-doped KMn8O16 on the surface of nickel foam. The triple-layer structure with Co-doped KMn8O16/Ni6MnO8/Ni foam was grown spontaneously on the surface of nickel foam by tuning the molar ratio of KMnO4 to Co(NO3)2·6H2O precursors. Importantly, the formed Ni6MnO8 structure between KMn8O16 and nickel foam during in situ synthesis process effectively protected nickel foam from further etching, which significantly enhanced the reaction stability of catalyst. The optimum amount of Co doping in KMn8O16 was available when the molar ratio of Mn to Co species in the precursor solution was 2:1. And the Mn2Co1 catalyst had abundant oxygen vacancies and excellent hydrophobicity, thus creating outstanding O3 decomposition activity. The O3 conversion under dry conditions and relative humidity of 65%, 90% over a period of 5 hr was 100%, 94% and 80% with the space velocity of 28,000 hr-1, respectively. The in situ constructed Co-doped KMn8O16/Ni foam catalyst showed the advantages of low price and gradual applicability of the preparation process, which provided an opportunity for the design of monolithic catalyst for O3 catalytic decomposition.

A new insight into the straw decomposition associated with minerals: Promoting straw humification and Cd immobilization.

Organic matter (OM) derived from the decomposition of crop residues plays a key role as a sorbent for cadmium (Cd) immobilization. Few studies have explored the straw decomposition processes with the presence of minerals, and the effect of newly generated organo-mineral complexes on heavy metal adsorption. In this study, we investigated the variations in structure and composition during the rice straw decomposition with or without minerals (goethite and kaolinite), as well as the adsorption behavior and mechanisms by which straw decomposition affects Cd immobilization. The degree of humification of extracted straw organic matter was assessed using excitation-emission matrix (EEM) fluorescence and Ultraviolet-visible spectroscopy (UV-vis), while employing FTIR spectroscopy and XPS to characterize the adsorption mechanisms. The spectra analysis revealed the enrichment of highly aromatic and hydrophobic components, indicating that the degree of straw decomposition and humification were further intensified during incubation. Additionally, the existence of goethite (SG) accelerated the humification of OM. Sorption experiments revealed that the straw humification increased Cd adsorption capacity. Notably, SG exhibited significantly higher adsorption performance compared to the organic matter without minerals (RS) and the existence of kaolinite (SK). Further analysis using FT-IR spectroscopy and XPS verified that the primary mechanisms involved in Cd immobilization were complexion with -OH and -COOH, as well as the formation of Cd-π binds with aromatic C=C on the surface of solid OMs. These findings will facilitate understanding the interactions of the rice straw decomposing with soil minerals and its remediation effect on Cd-contaminated farmland.

Enhancing visual seismocardiography in noisy environments with adaptive bidirectional filtering for Cardiac Health Monitoring.

BACKGROUND: Wearable sensors have revolutionized cardiac health monitoring, with Seismocardiography (SCG) at the forefront due to its non-invasive nature. However, the substantial motion artefacts have hindered the translation of SCG-based medical applications, primarily induced by walking. In contrast, our innovative technique, Adaptive Bidirectional Filtering (ABF), surpasses these challenges by refining SCG signals more effectively than any motion-induced noise. ABF leverages a noise-cancellation algorithm, operating on the benefits of the Redundant Multi-Scale Wavelet Decomposition (RMWD) and the bidirectional filtering framework, to achieve optimal signal quality. METHODOLOGY: The ABF technique is a two-stage process that diminishes the artefacts emanating from motion. The first step by RMWD is the identification of the heart-associated signals and the isolating samples with those related frequencies. Subsequently, the adaptive bidirectional filter operates in two dimensions: it uses Time-Frequency masking that eliminates temporal noise while engaging in non-negative matrix Decomposition to ensure spatial correlation and dorsoventral vibration reduction jointly. The main component that is altered from the other filters is the recursive structure that changes to the motion-adapted filter, which uses vertical axis accelerometer data to differentiate better between accurate SCG signals and motion artefacts. OUTCOME: Our empirical tests demonstrate exceptional signal improvement with the application of our ABF approach. The accuracy in heart rate estimation reached an impressive r-squared value of 0.95 at - 20 dB SNR, significantly outperforming the baseline value, which ranged from 0.1 to 0.85. The effectiveness of the motion-artifact-reduction methodology is also notable at an SNR of - 22 dB. Consequently, ECG inputs are not required. This method can be seamlessly integrated into noisy environments, enhancing ECG filtering, automatic beat detection, and rhythm interpretation processes, even in highly variable conditions. The ABF method effectively filters out up to 97% of motion-related noise components within the SCG signal from implantable devices. This advancement is poised to become an integral part of routine patient monitoring.

I-Spin live, an open-source software based on blind-source separation for real-time decoding of motor unit activity in humans.

Decoding the activity of individual neural cells during natural behaviours allows neuroscientists to study how the nervous system generates and controls movements. Contrary to other neural cells, the activity of spinal motor neurons can be determined non-invasively (or minimally invasively) from the decomposition of electromyographic (EMG) signals into motor unit firing activities. For some interfacing and neuro-feedback investigations, EMG decomposition needs to be performed in real time. Here, we introduce an open-source software that performs real-time decoding of motor neurons using a blind-source separation approach for multichannel EMG signal processing. Separation vectors (motor unit filters) are optimised for each motor unit from baseline contractions and then re-applied in real time during test contractions. In this way, the firing activity of multiple motor neurons can be provided through different forms of visual feedback. We provide a complete framework with guidelines and examples of recordings to guide researchers who aim to study movement control at the motor neuron level. We first validated the software with synthetic EMG signals generated during a range of isometric contraction patterns. We then tested the software on data collected using either surface or intramuscular electrode arrays from five lower limb muscles (gastrocnemius lateralis and medialis, vastus lateralis and medialis, and tibialis anterior). We assessed how the muscle or variation of contraction intensity between the baseline contraction and the test contraction impacted the accuracy of the real-time decomposition. This open-source software provides a set of tools for neuroscientists to design experimental paradigms where participants can receive real-time feedback on the output of the spinal cord circuits.

Task decomposition strategy based on machine learning for boosting performance and identifying mechanisms in heterogeneous activation of peracetic acid process.

Heterogeneous activation of peracetic acid (PAA) process is a promising method for removing organic pollutants from water. Nevertheless, this process is constrained by several complex factors, such as the selection of catalysts, optimization of reaction conditions, and identification of mechanism. In this study, a task decomposition strategy was adopted by combining a catalyst and reaction condition optimization machine learning (CRCO-ML) model and a mechanism identification machine learning (MI-ML) model to address these issues. The Categorical Boosting (CatBoost) model was identified as the best-performing model for the dataset (1024 sets and 7122 data points) in this study, achieving an R2 of 0.92 and an RMSE of 1.28. Catalyst composition, PAA dosage, and catalyst dosage were identified as the three most important features through SHAP analysis in the CRCO-ML model. The HCO3- is considered the most influential water matrix affecting the k value. The errors between all reverse experiment results and the predictions of the CRCO-ML and MI-ML models were <10 % and 15 %, respectively. This interdisciplinary work provides novel insights into the design and application of the heterogeneous activation of PAA process, significantly contributing to the rapid development of this technology.

From carrion to soil: microbial recycling of animal carcasses.

Decomposer microbial communities are gatekeepers in the redistribution of carbon and nutrients from dead animals (carrion) to terrestrial ecosystems. The flush of decomposition products from a carcass creates a hot spot of microbial activity in the soil below, and the animal's microbiome is released into the environment, mixing with soil communities. Changes in soil physicochemistry, especially reduced oxygen, temporarily constrain microbial nutrient cycling, and influence the timing of these processes and the fate of carrion resources. Carcass-related factors, such as mass, tissue composition, or even microbiome composition may also influence the functional assembly and succession of decomposer communities. Understanding these local scale microbially mediated processes is important for predicting consequences of carrion decomposition beyond the hot spot and hot moment.

Functional effects of subsidies and stressors on benthic microbial communities along freshwater to marine gradients.

Leaf litter in coastal wetlands lays the foundation for carbon storage, and the creation of coastal wetland soils. As climate change alters the biogeochemical conditions and macrophyte composition of coastal wetlands, a better understanding of the interactions between microbial communities, changing chemistry, and leaf litter is required to understand the dynamics of coastal litter breakdown in changing wetlands. Coastal wetlands are dynamic systems with shifting biogeochemical conditions, with both tidal and seasonal redox fluctuations, and marine subsidies to inland habitats. Here, we investigated gene expression associated with various microbial redox pathways to understand how changing conditions are affecting the benthic microbial communities responsible for litter breakdown in coastal wetlands. We performed a reciprocal transplant of leaf litter from four distinct plant species along freshwater-to-marine gradients in the Florida Coastal Everglades, tracking changes in environmental and litter biogeochemistry, as well as benthic microbial gene expression associated with varying redox conditions, carbon degradation, and phosphorus acquisition. Early litter breakdown varied primarily by species, with highest breakdown in coastal species, regardless of the site they were at during breakdown, while microbial gene expression showed a strong seasonal relationship between sulfate cycling and salinity, and was not correlated with breakdown rates. The effect of salinity is likely a combination of direct effects, and indirect effects from associated marine subsidies. We found a positive correlation between sulfate uptake and salinity during January with higher freshwater inputs to coastal areas. However, we found a peak of dissimilatory sulfate reduction at intermediate salinity during April when freshwater inputs to coastal sites are lower. The combination of these two results suggests that sulfate acquisition is limiting to microbes when freshwater inputs are high, but that when marine influence increases and sulfate becomes more available, dissimilatory sulfate reduction becomes a key microbial process. As marine influence in coastal wetlands increases with climate change, our study suggests that sulfate dynamics will become increasingly important to microbial communities colonizing decomposing leaf litter.

Attraction versus Repulsion between Methyl and Related Groups: (CH3NHCH3)2 and (CH3SeBr2CH3)2.

The starting point for this work was a set of crystal structures containing the motif of interaction between methyl groups in homodimers. Two structures were selected for which QTAIM, NCI and NBO analyses suggested an attractive interaction. However, the calculated interaction energy was negative for only one of these systems. The ability of methyl groups to interact with one another is then examined by DFT calculations. A series of (CH3PnHCH3)2 homodimers were allowed to interact with each other for a range of Pn atoms N, P, As, and Sb. Interaction energies of these C∙∙∙C tetrel-bonded species were below 1 kcal/mol, but could be raised to nearly 3 kcal/mol if the C atom was changed to a heavier tetrel. A strengthening of the C∙∙∙C intermethyl bonds can also be achieved by introducing an asymmetry via an electron-withdrawing substituent on one unit and a donor on the other. The attractions between the methyl and related groups occur in spite of a coulombic repulsion between σ-holes on the two groups. NBO, AIM, and NCI tools must be interpreted with caution as they can falsely suggest bonding when the potentials are repulsive.

Comparative analysis of electroencephalogram (EEG) data gathered from the frontal region with other brain regions affected by attention deficit hyperactivity disorder (ADHD) through multiresolution analysis and machine learning techniques.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by repeated patterns of hyperactivity, impulsivity, and inattention that limit daily functioning and development. Electroencephalography (EEG) anomalies correspond to changes in brain connection and activity. The authors propose utilizing empirical mode decomposition (EMD) and discrete wavelet transform (DWT) for feature extraction and machine learning (ML) algorithms to categorize ADHD and control subjects. For this study, the authors considered freely accessible ADHD data obtained from the IEEE data site. Studies have demonstrated a range of EEG anomalies in ADHD patients, such as variations in power spectra, coherence patterns, and event-related potentials (ERPs). Some of the studies claimed that the brain's prefrontal cortex and frontal regions collaborate in intricate networks, and disorders in either of them exacerbate the symptoms of ADHD. , Based on the research that claimed the brain's prefrontal cortex and frontal regions collaborate in intricate networks, and disorders in either of them exacerbate the symptoms of ADHD, the proposed study examines the optimal position of EEG electrode for identifying ADHD and in addition to monitoring accuracy on frontal/ prefrontal and other regions of brain our study also investigates the position groupings that have the highest effect on accurateness in identification of ADHD. The results demonstrate that the dataset classified with AdaBoost provided values for accuracy, precision, specificity, sensitivity, and F1-score as 1.00, 0.70, 0.70, 0.75, and 0.71, respectively, whereas using random forest (RF) it is 0.98, 0.64, 0.60, 0.81, and 0.71, respectively, in detecting ADHD. After detailed analysis, it is observed that the most accurate results included all electrodes. The authors believe the processes can detect various neurodevelopmental problems in children utilizing EEG signals.

Assessment and prediction of Water Quality Index (WQI) by seasonal key water parameters in a coastal city: application of machine learning models.

The Water Quality Index (WQI) provides comprehensive assessments in river systems; however, its calculation involves numerous water quality parameters, costly in sample collection and laboratory analysis. The study aimed to determine key water parameters and the most reliable models, considering seasonal variations in the water environment, to maximize the precision of WQI prediction by a minimal set of water parameters. Ten statistical or machine learning models were developed to predict the WQI over four seasons using water quality dataset collected in a coastal city adjacent to the Yellow Sea in China, based on which the key water parameters were identified and the variations were assessed by the Seasonal-Trend decomposition procedure based on Loess (STL). Results indicated that model performance generally improved with adding more input variables except Self-Organizing Map (SOM). Tree-based ensemble methods like Extreme Gradient Boosting (XGB) and Random Forest (RF) demonstrated the highest accuracy, particularly in winter. Nutrients (Ammonia Nitrogen (AN) and Total Phosphorus (TP)), Dissolved Oxygen (DO), and turbidity were determined as key water parameters, based on which, the prediction accuracy for Medium and Low grades was perfect while it was over 80% for the Good grade in spring and winter and dropped to around 70% in summer and autumn. Nutrient concentrations were higher at inland stations; however, it worsened at coastal stations, especially in summer. The study underscores the importance of reliable WQI prediction models in water quality assessment, especially when data is limited, which are crucial for managing water resources effectively.

Ecological drivers of carrion beetle (Staphylinidae: Silphinae) diversity on small to large mammals.

Silphinae (Staphylinidae; carrion beetles) are important contributors to the efficient decomposition and recycling of carrion necromass. Their community composition is important for the provision of this ecosystem function and can be affected by abiotic and biotic factors. However, investigations are lacking on the effects of carrion characteristics on Silphinae diversity. Carrion body mass may affect Silphinae diversity following the more individuals hypothesis (MIH). The MIH predicts a higher number of species at larger carrion because higher numbers of individuals can be supported on the resource patch. Additionally, biotic factors like carrion species identity or decomposition stage, and the abiotic factors elevation, season and temperature could affect Silphinae diversity. To test the hypotheses, we collected Silphinae throughout the decomposition of 100 carcasses representing 10 mammal species ranging from 0.04 to 124 kg. Experimental carcasses were exposed in a mountain forest landscape in Germany during spring and summer of 2021. We analysed Silphinae diversity using recently developed transformation models that considered the difficult data distribution we obtained. We found no consistent effect of carrion body mass on Silphinae species richness and, therefore, rejected the MIH. Carrion decomposition stage, in contrast, strongly influenced Silphinae diversity. Abundance and species richness increased with the decomposition process. Silphinae abundance increased with temperature and decreased with elevation. Furthermore, Silphinae abundance was lower in summer compared to spring, likely due to increased co-occurrence and competition with dipteran larvae in summer. Neither carrion species identity nor any abiotic factor affected Silphinae species richness following a pattern consistent throughout the seasons. Our approach combining a broad study design with an improved method for data analysis, transformation models, revealed new insights into mechanisms driving carrion beetle diversity during carrion decomposition. Overall, our study illustrates the complexity and multifactorial nature of biotic and abiotic factors affecting diversity.

Hybrid similarity based feature selection and cascade deep maxout fuzzy network for Autism Spectrum Disorder detection using EEG signal.

Autism Spectrum Disorder (ASD) is a neurological disorder that influences a person's comprehension and way of behaving. It is a lifetime disability that cannot be completely treated using any therapy up to date. Nevertheless, in time identification and continuous therapies have a huge effect on autism patients. The existing models took a long time to confirm the diagnosis process and also, it is highly complex to differentiate autism from various developmental disorders. To facilitate early diagnosis by providing timely intervention, saving healthcare costs and reducing stress for the family in the long run, this research introduces an affordable and straightforward diagnostic model to detect ASD using EEG and deep learning models. Here, a hybrid deep learning model called Cascade deep maxout fuzzy network (Cascade DMFN) is proposed to identify ASD and it is achieved by the integration of Deep Maxout Network (DMN) and hybrid cascade neuro-fuzzy. Moreover, hybrid similarity measures like Canberra distance and Kumar-hassebrook is employed to conduct the feature selection technique. Also, the EEG dataset and BCIAUT_P300 dataset are used for analyzing the designed Cascade DMFN for detecting Autism Spectrum Disorder. The designed Cascade DMFN has outperformed other classical models by yielding a high accuracy of 0.930, Negative Predictive Value (NPV) of 0.919, Positive Predictive Value (PPV) of 0.923, True Negative Rate (TNR) of 0.926, and True Positive Rate (TPR) of 0.934.

Taphonomic impact of vertebrate scavengers on degradation and dispersal of remains, southeastern British Columbia.

Vertebrate scavengers represent important taphonomic agents that can act on a body, particularly when in an outdoor environment. Understanding the effects of these agents will direct how and where to search for human remains and influence the likelihood of discovery in a particular region. The current study aimed to identify the taphonomic impact of scavenger guilds in the peri-urban and rural regions of southeastern British Columbia. Vertebrate scavenger activity on pig carcasses was recorded remotely using trail cameras and analyzed to determine temporal scavenging profiles. Both the peri-urban and rural environments produced comparable scavenger guilds, namely: turkey vultures, American crows/northern ravens (classified as "corvids"), American black bears, and coyotes. Although the two locations had different study lengths due to variable degrees of scavenging, for the period that was common to both locations (summer to early fall), the black bear was the most frequent scavenger followed by coyote. However, the dispersal of remains by the mammalian scavengers was distinctly different between sites. Only 12%-33% of skeletal elements were recovered at the rural sites compared to 80%-90% recovered at the peri-urban sites, even though the latter sites had a longer study timeframe. The extended timeframe of the peri-urban sites confirmed that certain scavengers (e.g., turkey vultures and black bears) are only seasonally active in this region. These findings demonstrate the variability of scavenger behavior and the need to assign caution and local ecological knowledge when predicting scavenger trends. Such taphonomic information is relevant for human remains searches in regions with comparable scavenger guilds.

Eigenbin compression for reducing photon-counting CT data size.

BACKGROUND: Photon-counting CT (PCCT) systems acquire multiple spectral measurements at high spatial resolution, providing numerous image quality benefits while also increasing the amount of data that must be transferred through the gantry slip ring. PURPOSE: This study proposes a lossy method to compress photon-counting CT data using eigenvector analysis, with the goal of providing image quality sufficient for applications that require a rapid initial reconstruction, such as to confirm anatomical coverage, scan quality, and to support automated advanced applications. The eigenbin compression method was experimentally evaluated on a clinical silicon PCCT prototype system. METHODS: The proposed eigenbin method performs principal component analysis (PCA) on a set of PCCT calibration measurements. PCA finds the orthogonal axes or eigenvectors, which capture the maximum variance in the N dimensional photon-count data space, where N is the number of acquired energy bins. To reduce the dimensionality of the PCCT data, the data are linearly transformed into a lower dimensional space spanned by the M < N eigenvectors with highest eigenvalues (i.e., the vectors that account for most of the information in the data). Only M coefficients are then transferred per measurement, which we term eigenbin values. After transmission, the original N energy-bin measurements are estimated as a linear combination of the M eigenvectors. Two versions of the eigenbin method were investigated: pixel-specific and pixel-general. The pixel-specific eigenbin method determines eigenvectors for each individual detector pixel, while the more practically realizable pixel-general eigenbin method finds one set of eigenvectors for the entire detector array. The eigenbin method was experimentally evaluated by scanning a 20 cm diameter Gammex Multienergy phantom with different material inserts on a clinical silicon-based PCCT prototype. The method was evaluated with the number of eigenbins varied between two and four. In each case, the eigenbins were used to estimate the original 8-bin data, after which material decomposition was performed. The mean, standard deviation, and contrast-to-noise ratio (CNR) of values in the reconstructed basis and virtual monoenergetic images (VMI) were compared for the original 8-bin data and for the eigenbin data. RESULTS: The pixel-specific eigenbin method reduced photon-counting CT data size by a factor of four with <5% change in mean values and a small noise penalty (mean change in noise of <12%, maximum change in noise of 20% for basis images). The pixel-general eigenbin compression method reduced data size by a factor of 2.67 with <5% change in mean values and a less than 10% noise penalty in the basis images (average noise penalty ≤5%). The noise penalty and errors were less for the VMIs than for the basis images, resulting in <5% change in CNR in the VMIs. CONCLUSION: The eigenbin compression method reduced photon-counting CT data size by a factor of two to four with less than 5% change in mean values, noise penalty of less than 10%-20%, and change in CNR ranging from 15% decrease to 24% increase. Eigenbin compression reduces the data transfer time and storage space of photon-counting CT data for applications that require rapid initial reconstructions.

Effect of straw decomposition on hexavalent chromium removal by straw: Significant roles of surface potential and dissolved organic matter.

Mobility and bioavailability of hexavalent chromium (Cr(VI)) in agricultural soils are affected by interactions between Cr(VI) and returned crop straws. However, the effect of straw decomposition on Cr(VI) removal and underlying mechanisms remain unclear. In this study, Cr(VI) removal by pristine and decomposed rice/rape straws was investigated by batch experiments and a series of spectroscopies. The results showed that straw decomposition inhibited Cr(VI) removal, regardless of straw types. However, the potential mechanisms of the inhibition were distinct for the two straws. For the rice straw, a lower zeta potential after decomposition suppressed Cr(VI) sorption and subsequent reduction. In addition, less Cr(VI) was reduced by the decomposed rice straw-derived dissolved organic matter (DOM) than the pristine one. In contrast, for the rape straw, due to the increased zeta potential after decomposition, the decreased Cr(VI) removal was mainly ascribed to less Cr(VI) reduction by the rape straw-derived DOM. These results emphasized the significant roles of straw surface potential and DOM in Cr(VI) removal, depending on straw types and decomposition, which facilitate the fundamental understanding of Cr(VI) removal by straws and are helpful for predicting the environmental risk of Cr and rational straw return in Cr(VI)-contaminated fields.

What explains differences in average wait time in the emergency department among different racial and ethnic populations: A linear decomposition approach.

Objective: Non-Hispanic Black (NHB) and Hispanic/Latino (Hispanic) patients wait longer in the emergency department (ED) to see practitioners when compared with non-Hispanic White (NHW) patients. We investigate factors contributing to longer wait times for NHB and Hispanic patients using a linear decomposition approach. Methods: This retrospective observational study included patients presenting to one tertiary hospital ED from 2019 to 2021. Median wait times among NHW, NHB, and Hispanic were calculated with multivariable linear regressions. The extent to which demographic, clinical, and hospital factors explained the differences in average wait time among the three groups were analyzed with Blinder‒Oaxaca post-linear decomposition model. Results: There were 310,253 total patients including 34.7% of NHW, 34.7% of NHB, and 30.6% of Hispanic patients. The median wait time in NHW was 9 min (interquartile range [IQR] 4‒47 min), in NHB was 13 min (IQR 4‒59 min), and in Hispanic was 19 min (IQR 5‒78 min, p < 0.001). The top two contributors of average wait time difference were mode of arrival and triage acuity level. Post-linear decomposition analysis showed that 72.96% of the NHB‒NHW and 87.77% of the Hispanic‒NHW average wait time difference were explained by variables analyzed. Conclusion: Compared to NHW patients, NHB and Hispanic patients typically experience longer ED wait times, primarily influenced by their mode of arrival and triaged acuity levels. Despite these recognized factors, there remains 12%‒27% unexplained factors at work, such as social determinants of health (including implicit bias and systemic racism) and many other unmeasured confounders, yet to be discovered.