Analyzing ozone formation sensitivity in a typical industrial city in China: Implications for effective source control in the chemical transition regime.

Volatile organic compounds (VOCs) play a major role in O3 formation in urban environments. However, the complexity in the emissions of VOCs and nitrogen oxides (NOx) in industrial cities has made it challenging to identify the key factors influencing O3 formation. This study used observation-based-model (OBM) to analyze O3 sensitivities to VOCs and NOx during summer in a typical industrial city in China. The OBM model results were coupled with a receptor model to analyze the sources of O3. Higher concentrations of O3 precursors were observed during polluted periods indicating that precursor accumulation contributed to the higher maxima of the net ozone formation rate and HOx concentrations. Analyses of ROx· budgets and relative incremental reactivity (RIR) indicated that O3 production is in a chemical transition regime and was sensitive to both VOCs and NOx. Results from Positive Matrix Factorization (PMF) analysis indicated that gasoline vehicle emissions, industrial processes, and coal combustion were major sources of O3 precursors. The sensitivities of O3 production to these sources depend on if both VOC and NOx sensitivities are considered. If only VOCs sensitivity is considered, in contrast, the contribution of anthropogenic sources to O3 production was significantly underestimated. This study highlights the importance of accounting for both VOCs and NOx sensitivities when O3 chemistry is in a transition regime in O3 production attribution studies.

Technological progress and coupling renewables enable substantial environmental and economic benefits from coal-to-olefins.

China's growing demand for bulk chemicals and concerns regarding energy security are scaling up coal-to-olefins (CTO) production. Three generations of independent dimethyl ether/methanol-to-olefins technologies have been successively launched with greatly improved production efficiencies. However, to date, widespread concerns regarding the intensive environmental impacts and potential economic risks have not been addressed in the context of this industrialization. Here we show that, through the technological progress from the first to the third generation, life cycle energy consumption, water consumption, and carbon emissions can be reduced to 119.5 GJ/t, 27.6 t/t, and 9.1 t CO2-eq/t, respectively, and human health damage, ecosystem quality damage, and resource scarcity impacts can be decreased by 40.5 %, 50.1 %, and 16.4 %, respectively. This is accompanied by an excellent performance in terms of production cost, net present value, and internal return rate at 792.5 USD/t, 173.4 USD/t, and 19.4 %, respectively. Substantial environmental and economic benefits can be gained by coupling renewables in the form of using green hydrogen from solar and wind power to synthesize methanol. Particularly, life cycle carbon emissions and resource scarcity impacts are reduced by 23.4 % and 22.4 %, respectively, exceeding the reduction in technological progress. However, coupling renewables increases the life cycle energy consumption to 154.5 GJ/t, counteracting the benefits of technological progress. Our results highlight the importance of technological progress and coupled renewables for enhancing the sustainability of the CTO industry.

ART2Dose: A comprehensive dose verification platform for online adaptive radiotherapy.

BACKGROUND: Online adaptive radiotherapy (ART) involves the development of adaptable treatment plans that consider patient anatomical data obtained right prior to treatment administration, facilitated by cone-beam computed tomography guided adaptive radiotherapy (CTgART) and magnetic resonance image-guided adaptive radiotherapy (MRgART). To ensure accuracy of these adaptive plans, it is crucial to conduct calculation-based checks and independent verification of volumetric dose distribution, as measurement-based checks are not practical within online workflows. However, the absence of comprehensive, efficient, and highly integrated commercial software for secondary dose verification can impede the time-sensitive nature of online ART procedures. PURPOSE: The main aim of this study is to introduce an efficient online quality assurance (QA) platform for online ART, and subsequently evaluate it on Ethos and Unity treatment delivery systems in our clinic. METHODS: To enhance efficiency and ensure compliance with safety standards in online ART, ART2Dose, a secondary dose verification software, has been developed and integrated into our online QA workflow. This implementation spans all online ART treatments at our institution. The ART2Dose infrastructure comprises four key components: an SQLite database, a dose calculation server, a report generator, and a web portal. Through this infrastructure, file transfer, dose calculation, report generation, and report approval/archival are seamlessly managed, minimizing the need for user input when exporting RT DICOM files and approving the generated QA report. ART2Dose was compared with Mobius3D in pre-clinical evaluations on secondary dose verification for 40 adaptive plans. Additionally, a retrospective investigation was conducted utilizing 1302 CTgART fractions from ten treatment sites and 1278 MRgART fractions from seven treatment sites to evaluate the practical accuracy and efficiency of ART2Dose in routine clinical use. RESULTS: With dedicated infrastructure and an integrated workflow, ART2Dose achieved gamma passing rates that were comparable to or higher than those of Mobius3D. Additionally, it significantly reduced the time required to complete pre-treatment checks by 3-4 min for each plan. In the retrospective analysis of clinical CTgART and MRgART fractions, ART2Dose demonstrated average gamma passing rates of 99.61 ± 0.83% and 97.75 ± 2.54%, respectively, using the 3%/2 mm criteria for region greater than 10% of prescription dose. The average calculation times for CTgART and MRgART were approximately 1 and 2 min, respectively. CONCLUSION: Overall, the streamlined implementation of ART2Dose notably enhances the online ART workflow, offering reliable and efficient online QA while reducing time pressure in the clinic and minimizing labor-intensive work.

Modeling gamma knife radiosurgical toxicity for multiple brain metastases.

BACKGROUND AND PURPOSE: Radiation oncology protocols for single fraction radiosurgery recommend setting dosing criteria based on assumed risk of radionecrosis, which can be predicted by the 12 Gy normal brain volume (V12). In this study, we show that tumor surface area (SA) and a simple power-law model using only preplan variables can estimate and minimize radiosurgical toxicity. MATERIALS AND METHODS: A 245-patient cohort with 1217 brain metastases treated with single or distributed Gamma Knife sessions was reviewed retrospectively. Univariate and multivariable linear regression models and power-law models determined which modeling parameters best predicted V12. The V12 power-law model, represented by a product of normalized Rx dose Rxn, and tumor longest axial dimension LAD (V12 âˆ¼ Rxn1.5*LAD2), was independently validated using a secondary 63-patient cohort with 302 brain metastases. RESULTS: Surface area was the best univariate linear predictor of V12 (adjR2 = 0.770), followed by longest axial dimension (adjR2 = 0.755) and volume (adjR2 = 0.745). The power-law model accounted for 90% variance in V12 for 1217 metastatic lesions (adjR2 = 0.906) and 245 patients (adjR2 = 0.896). The average difference ΔV12 between predicted and measured V12s was (0.28 ± 0.55) cm3 per lesion and (1.0 ± 1.2) cm3 per patient. The power-law predictive capability was validated using a secondary 63-patient dataset (adjR2 = 0.867) with 302 brain metastases (adjR2 = 0.825). CONCLUSION: Surface area was the most accurate univariate predictor of V12 for metastatic lesions. We developed a preplan model for brain metastases that can help better estimate radionecrosis risk, determine prescription doses given a target V12, and provide safe dose escalation strategies without the use of any planning software.

[Revealing Driving Factors of Urban O3 Based on Explainable Machine Learning].

Driven by precursor emissions, meteorological conditions, and other factors, atmospheric ozone (O3) has become the main pollutant affecting urban air quality in summer. The current deductive models driven by physical and chemical mechanisms require a large number of parameters for the analysis of O3 pollution, and the calculation timeliness is poor. The data-driven inductive models are efficient but have problems such as poor explanation. In this study, an explainable model of data-driven Correlation-ML-SHAP was established to reveal the strongly correlated influencing factors of O3 concentration. Additionally, the machine learning ML module coupled with the explainable SHAP module was used to calculate the contributions of driving factors to O3 concentration, so as to realize the quantitative analysis of driving factors. The O3 pollution process in the summer of 2021 in Jincheng City was used as an example to carry out the application research. The results showed that the Correlation-ML-SHAP model could reveal and use strong driving factors to simulate O3 concentration and quantify influence contribution, and the ML module used the XGBoost model to achieve the best simulation accuracy. Air temperature, solar radiation, relative humidity, and precursor emission level were the strong driving factors of O3 pollution in Jincheng City in summer 2021, and the contribution weights were 32.1%, 21.3%, 16.5%, and 15.6%. The contribution weights of air temperature, solar radiation, and precursor emission level increased by 3.4%, 1.2%, and 1.2% on polluted days, respectively, and the contribution weights of precursor emission level rose to third place on polluted days. Each driving factor had a nonlinear interaction effect on O3 concentration. When the air temperature exceeded 24℃, or the relative humidity was lower than 70%, there was a 94.9% and 94.1% probability of positive contribution to O3 pollution, respectively. Under such meteorological conditions, ρ(NO2) exceeded 9 µg·m-3, or ρ(CO) exceeded 0.7 mg·m-3, and there was a 94.9% and 99.3% probability of positive contribution to O3 pollution, respectively. The southeast wind speed was lower than 5.8 m·s-1, or the south wind speed was lower than 5.3 m·s-1, both of which contributed positively to O3 pollution. The model quantitatively analyzed the influence contribution of various driving factors on urban O3 concentration, which could provide a basis for the prevention and control of urban atmospheric O3 pollution in summer.

Bony structure enhanced synthetic CT generation using Dixon sequences for pelvis MR-only radiotherapy.

BACKGROUND: MRI-only radiotherapy planning (MROP) is beneficial to patients by avoiding MRI/CT registration errors, simplifying the radiation treatment simulation workflow and reducing exposure to ionizing radiation. MRI is the primary imaging modality for soft tissue delineation. Treatment planning CTs (i.e., CT simulation scan) are redundant if a synthetic CT (sCT) can be generated from the MRI to provide the patient positioning and electron density information. Unsupervised deep learning (DL) models like CycleGAN are widely used in MR-to-sCT conversion, when paired patient CT and MR image datasets are not available for model training. However, compared to supervised DL models, they cannot guarantee anatomic consistency, especially around bone. PURPOSE: The purpose of this work was to improve the sCT accuracy generated from MRI around bone for MROP. METHODS: To generate more reliable bony structures on sCT images, we proposed to add bony structure constraints in the unsupervised CycleGAN model's loss function and leverage Dixon constructed fat and in-phase (IP) MR images. Dixon images provide better bone contrast than T2-weighted images as inputs to a modified multi-channel CycleGAN. A private dataset with a total of 31 prostate cancer patients were used for training (20) and testing (11). RESULTS: We compared model performance with and without bony structure constraints using single- and multi-channel inputs. Among all the models, multi-channel CycleGAN with bony structure constraints had the lowest mean absolute error, both inside the bone and whole body (50.7 and 145.2 HU). This approach also resulted in the highest Dice similarity coefficient (0.88) of all bony structures compared with the planning CT. CONCLUSION: Modified multi-channel CycleGAN with bony structure constraints, taking Dixon-constructed fat and IP images as inputs, can generate clinically suitable sCT images in both bone and soft tissue. The generated sCT images have the potential to be used for accurate dose calculation and patient positioning in MROP radiation therapy.

Effect of Hollow 304 Stainless Steel Fiber on Corrosion Resistance and Mechanical Properties of Ultra-High Performance Concrete (UHPC).

This study investigated the effect of hollow 304 stainless-steel fiber on the corrosion resistance and mechanical properties of ultra-high-performance concrete (UHPC), and prepared copper-coated-fiber-reinforced UHPC as the control group. The electrochemical performance of the prepared UHPC was compared with the results of X-ray computed tomography (X-CT). The results reveal that cavitation can improve the distribution of steel fibers in the UHPC. Compared with solid steel fibers, the compressive strength of UHPC with hollow stainless-steel fibers did not exhibit significant change, but the maximum flexural strength increased by 45.2% (2 vol% content, length-diameter ratio of 60). Hollow stainless-steel fiber could better improve the durability of UHPC compared with copper-plated steel fiber, and the gap between the two continued to increase as the durability test progressed. After the dry-wet cycle test, the flexural strength of the copper-coated-fiber-reinforced UHPC was 26 MPa, marking a decrease of 21.9%, while the flexural strength of the UHPC mixed with hollow stainless-steel fibers was 40.1 MPa, marking a decrease of only 5.6%. When the salt spray test had run for seven days, the difference in the flexural strength between the two was 18.4%, but when the test ended (180 days), the difference increased to 34%. The electrochemical performance of the hollow stainless-steel fiber improved, owing to the small carrying capacity of the hollow structure, and more uniform distribution in the UHPC and lower interconnection probability were achieved. According to the AC impedance test results, the charge transfer impedance of the UHPC doped with solid steel fiber is 5.8 KΩ, while that of the UHPC doped with hollow stainless-steel fiber is 8.8 KΩ.

Comparison of the Mechanical Properties and Crack Expansion Mechanism of Different Content and Shapes of Brass-Coated Steel Fiber-Reinforced Ultra-High-Performance Concrete.

Steel fiber-reinforced ultra-high-performance concrete (UHPC) is becoming an important type of concrete reinforcement. After mixing with the reinforced steel fibers, the UHPC has perfect flex resistance, shear strength, crack resistance, shock resistance, and anti-seepage. In this study, the influence of straight, corrugated, and hooked brass-coated steel fibers (BCSFs) on the microstructure, mechanical properties, and crack expansion mechanism of ultra-high-performance concrete (UHPC) with varying content of 1-6 wt.% under different curing times were investigated. Field emission scanning electron microscopy and energy dispersive X-ray spectrometry were employed to characterize the microstructure of the BCSF-reinforced UHPC mix specimens. X-ray computed tomography was employed to determine the porosity of the BCSF-reinforced UHPC mix specimens. The obtained results indicate the flexural strength and compressive strength of BCSF-reinforced UHPC mix specimens are enhanced, along with increasing the content of BCSFs reinforcement with different shapes (straight, corrugated, and hooked). The embedded BCSFs play a major role in the adhesive property and stress transfer of the BCSFs-UHPC matrix interface. Different from many studies, the flexural strength of mix UHPC with straight BCSFs is higher than those with corrugated and hooked BCSFs. However, the compressive strength of UHPC with corrugated BCSFs is higher than those with straight and hooked BCSFs. The flexural strength of mix UHPC with 6 wt.% straight BCSFs at 28 days reaches the maximum value of 26.2 MPa, and the compressive strength of UHPC with 6 wt.% corrugated BCSFs at 28 days reaches the maximum value of 142.3 MPa. With the increase in straight BCSF content from 1 wt.% to 6 wt.%, the porosity in mix UHPC reduces gradually from 18.4% to 8.3%. The length of average crack spacing is dependent on the straight BCSF content. With the increase in straight BCSF content from 1 wt.% to 6 wt.%, the average crack length reduces gradually from 34.2 mm to 12.1 mm, and the average crack width reduces gradually from 0.78 mm to less than 0.1 mm. During crack extension, part of the energy in the UHPC mixture specimen with the 6 wt.% BCSF content flows into the crack tip region converted into the work dissipated during the bridging process. The crack propagation resistance of the UHPC mixture with straight BCSFs was improved compared with those with corrugated and hooked BCSFs. The bond strength between the BCSFs and UHPC matrix was enhanced by using vibrational mixing, and the brass film coated on the BCSFs contributes to increase the flexural and compressive strength of the UHPC mixture.

Impact of primary emission variations on secondary inorganic aerosol formation: Prospective from COVID-19 lockdown in a typical northern China city.

Hourly observations in northern China city of Taiyuan were performed to compare secondary inorganic aerosol (SIA) reaction mechanisms, and emission effects on SIA during the pre-lock and COVID-19 lock days. Emission control implemented and meteorological conditions during lock days both caused beneficial impact on air quality. NO2 showed the highest decrease ratio of -49.5%, while the relative fraction of NO3- in PM2.5 increased the most (2.7%). Source apportionment revealed the top three contributors to PM2.5 were secondary formation (SF), coal combustion (CC), and vehicle exhaust (VE) during both pre-lock and lock days. EC lock/pre were all lower than 1, suggesting the overall reduction of primary emissions during lock days, while the higher ratio of (SIA/EC) lock/pre (1.01-1.36) indicated the enhanced secondary formation in lock days. The ratio of SIA of pollution to clean days during lock periods considerably higher by 23.7% compared with that in pre-lock periods, which was indicated SIA secondary formation was more pronounced and contributed great to pollution days in lock periods though secondary formation existed in pre-lock and lock periods. Enhanced secondary formation of NO3- and SO42- during lock days might be mainly due to the increased in aqueous and gas-phase reactions, respectively. Except for SF, high contribution of VE and CC were also important for high SIA concentration in pre-lock and lock days, respectively. The decreased contribution of VE weakens its contribution to SIA formation, indicating the effectiveness of VE emission control, as confirmed during the COVID-19 pandemic. This study highlights the aqueous and gas-phase reactions for nitrate and sulfate, respectively, which contributed to heavy pollution, as well as indicated the important role of VE on SIA formation, suggesting the urgent need to further strengthen controls on vehicle emissions.

Decomposition and humification process of domestic biodegradable waste by black soldier fly (Hermetia illucens L.) larvae from the perspective of dissolved organic matter.

Black soldier fly larvae (Hermitia illucens L.) (BSFL) bioconversion is a promising technology for domestic biodegradable waste (DBW) management and resource recovery. However, little is known about the DBW biodegradation during the BSFL bioconversion from the perspective of dissolved organic matter (DOM). In the current study, field tests were conducted on a full-scale BSFL bioconversion facility with treatment capacity of 15 tons DBW/day. Composition of DOM in DBW were investigated by spectral analysis (UV-vis, fluorescence, and Fourier Transform Infrared spectroscopy (FT-IR)), coupled with enzyme activity analysis. After BSFL bioconversion, DOM concentrations, total carbon and total nitrogen in residues decreased by 51.5%, 18.3% and 19.9%, respectively. Meanwhile, enzymes like catalase, lipase, protease, sucrase, urease and cellulase significantly increased (9.28%-56.3%). The specific UV absorbance at 254 nm and 280 nm (SUVA254, SUVA280), the area at 226-400 nm (A226-400) and slope in the 280-400 nm region (S280-400) of DOM increased by 230%, 186%, 143% and 398%, respectively. Moreover, the characteristic peaks at 1636, 1077 and 1045 cm-1 in FT-IR increased continuously, with a significant decrease in peak at 1124 and 1572 cm-1. DOM spectral data show that BSFL decomposed the carboxylic, cellulose and aliphatic components, resulting in the increase of oxygen-containing functional groups (e.g., hydroxyl, carboxyl, carbonyl) and aromatic compounds. Furthermore, fluorescence profiles show that Region Ⅰ, Ⅱ (aromatic proteins) and Ⅳ (soluble microbial byproducts) decreased while Region Ⅴ (humic-like substances) increased significantly. Humification index increased by 122% while biological index decreased by 18.0%, indicating a significant increase in degree of humification and stabilization of the residues. The current evidence provides a theoretical basis for technical re-innovation and improving economic potential of BSFL technology.

Preliminary Evaluation of PTV Margins for Online Adaptive Radiation Therapy of the Prostatic Fossa.

PURPOSE: In modern trials, traditional planning target volume (PTV) margins for postoperative prostate radiation therapy have been large (7-10 mm) to account for both daily changes in patient positioning and target deformation. With daily adaptive radiation therapy, these interfractional changes could be minimized, potentially reducing the margins required for treatment and improving adjacent normal-tissue dosimetry. METHODS AND MATERIALS: A single-center retrospective study was conducted from March 2021 to November 2021. Patients receiving conventionally fractionated postoperative radiation therapy (PORT) for prostate cancer with pretreatment and posttreatment cone beam computed tomography (CBCT) imaging (pre-CBCT and post-CBCT, respectively) were included (248 paired images). Pretreatment and posttreatment clinical target volumes (pre-CTVs and post-CTVs) were contoured by a single observer on all CBCTs and verified by a second observer. Motion was calculated from pre-CTV to that of the post-CTV, and predicted margins were calculated with van Herk's formula. Adequate coverage of the proposed planning target volume (PTV) margin expansions (pre-PTV) were verified by determining overlap with post-CTV. In a smaller cohort (25 paired images), dosimetric changes with the proposed online adaptive margins were compared with conventional plans in the Ethos emulator environment. RESULTS: The estimated margins predicted to achieve ≥95% CTV coverage for 90% of the population were 1.6 mm, 2.0 mm, and 2.2 mm (x-, y-, and z -xes, respectively), with 95% of the absolute region of interest displacement being within 1.9 mm, 2.8 mm, and 2.1 mm. After symmetrically expanding all pre-CTVs by 3 mm, the percentage of paired images achieving ≥95% CTV coverage was 97.1%. When comparing adaptive plans (3-mm margins) with scheduled plans (7-mm margins), rectum dosimetry significantly improved, with an average relative reduction in V40Gy[cc] of 59.2% and V65Gy[cc] of 79.5% (where V40Gy and V65Gy are defined as the volumes receiving 40 Gy and 65 Gy or higher dose, respectively). CONCLUSIONS: Online daily adaptive radiation therapy could significantly decrease PTV margins for prostatic PORT and improve rectal dosimetry, with a symmetrical expansion of 3 mm achieving excellent coverage in this cohort. These results need to be validated in a larger prospective cohort.

Ensemble learning and personalized training for the improvement of unsupervised deep learning-based synthetic CT reconstruction.

BACKGROUND: The growing adoption of magnetic resonance imaging (MRI)-guided radiation therapy (RT) platforms and a focus on MRI-only RT workflows have brought the technical challenge of synthetic computed tomography (sCT) reconstruction to the forefront. Unpaired-data deep learning-based approaches to the problem offer the attractive characteristic of not requiring paired training data, but the gap between paired- and unpaired-data results can be limiting. PURPOSE: We present two distinct approaches aimed at improving unpaired-data sCT reconstruction results: a cascade ensemble that combines multiple models and a personalized training strategy originally designed for the paired-data setting. METHODS: Comparisons are made between the following models: (1) the paired-data fully convolutional DenseNet (FCDN), (2) the FCDN with the Intentional Deep Overfit Learning (IDOL) personalized training strategy, (3) the unpaired-data CycleGAN, (4) the CycleGAN with the IDOL training strategy, and (5) the CycleGAN as an intermediate model in a cascade ensemble approach. Evaluation of the various models over 25 total patients is carried out using a five-fold cross-validation scheme, with the patient-specific IDOL models being trained for the five patients of fold 3, chosen at random. RESULTS: In both the paired- and unpaired-data settings, adopting the IDOL training strategy led to improvements in the mean absolute error (MAE) between true CT images and sCT outputs within the body contour (mean improvement, paired- and unpaired-data approaches, respectively: 38%, 9%) and in regions of bone (52%, 5%), the peak signal-to-noise ratio (PSNR; 15%, 7%), and the structural similarity index (SSIM; 6%, <1%). The ensemble approach offered additional benefits over the IDOL approach in all three metrics (mean improvement over unpaired-data approach in fold 3; MAE: 20%; bone MAE: 16%; PSNR: 10%; SSIM: 2%), and differences in body MAE between the ensemble approach and the paired-data approach are statistically insignificant. CONCLUSIONS: We have demonstrated that both a cascade ensemble approach and a personalized training strategy designed initially for the paired-data setting offer significant improvements in image quality metrics for the unpaired-data sCT reconstruction task. Closing the gap between paired- and unpaired-data approaches is a step toward fully enabling these powerful and attractive unpaired-data frameworks.

Machine characterization and central axis depth dose data of a superficial x-ray radiotherapy unit.

Objectives. The purpose of this study is to present data from the clinical commissioning of an Xstrahl 150 x-ray unit used for superficial radiotherapy,Methods. Commissioning tasks included vendor acceptance tests, timer reproducibility, linearity and end-effect measurements, half-value layer (HVL) measurements, inverse square law verification, head-leakage measurements, and beam output calibration. In addition, percent depth dose (PDD) curves were determined for different combinations of filter/kV settings and applicators. Automated PDD water phantom scans were performed utilizing four contemporary detectors: a microDiamond detector, a microSilicon detector, an EDGE detector, and a PinPoint ionization chamber. The measured PDD data were compared to the published values in BJR Supplement 25,Results. The x-ray unit's mechanical, safety, and radiation characteristics were within vendor-stated specifications. Across sixty commissioned x-ray beams, the PDDs determined in water using solid state detectors were in excellent agreement with the BJR 25 data. For the lower (<100 kVp) and medium-energy (≥100 kVp) superficial beams the average agreement was within [-3.6,+0.4]% and [-3.7,+1.4]% range, respectively. For the high-energy superficial (low-energy orthovoltage) x-rays at 150 kVp, the average difference for the largest 20 × 20 cm2collimator was (-0.7 ± 1.0)%,Conclusions. This study presents machine characterization data collected for clinical use of a superficial x-ray unit. Special focus was placed on utilizing contemporary detectors and techniques for the relative PDD measurements using a motorized water phantom. The results in this study confirm that the aggregate values published in the BJR 25 report still serve as a valid benchmark when comparing data from site-specific measurements, or the reference data for clinical utilization without such measurements,Advances in knowledge. This paper presents comprehensive data from the acceptance and commissioning of a modern kilovoltage superficial x-ray radiotherapy machine. Comparisons between the PDD data measured in this study using different detectors and BJR 25 data are highlighted.

Effects of water levels on plant traits and nitrogen use efficiency in monoculture and intercropped artificial grasslands.

Water availability is the main factor affecting the forage productivity of artificial grasslands, particularly in semi-arid regions. Generally, intercropping of gramineous grass and leguminous grass can achieve high productivity. However, how different water availability levels affect the productivity of intercropping system remains unclear. Here, we conducted a 3-year (2015-2017) study by manipulating the water conditions (CK equivalent to the annual precipitation, +50% treatment equivalent to 50% increase over the average precipitation, and -50% treatment equivalent to 50% decrease over the average precipitation) to explore the responses of plant traits, nitrogen use efficiency, and biomass of the monoculture of Medicago sativa (a leguminous grass, M.s), monoculture of Elymus nutans (a gramineous grass, E.n), and intercropping of M.s and E.n in a semi-arid region in Inner Mongolia, China. The results showed that the biomass obtained by intercropping of M.s and E.n decreased by 24.4% in -50% treatment compared to the CK treatment, while that of the monoculture of M.s decreased by 34.4% under the -50% treatment compared to the CK treatment. However, there was no significant difference in the biomass between intercropping artificial grassland and monoculture M. sativa under +50% treatment. Compared to monoculture, M.s can obtain more nitrogen by biological nitrogen fixation and decrease the proportion of nitrogen absorbed from soils under intercropping in the same water conditions. Under the intercropping system, the proportions of nitrogen absorbed from soils by M.s were 87.4%, 85.1, and 76.9% in -50%, CK, and +50% treatments, respectively. Under monoculture, these proportions were 91.9, 89.3, and 82.3% in -50%, CK, and +50% treatments, respectively. Plant trait, but not soil nitrogen content, was the main regulator for the productivity responses to water level changes. Our results highlight that intercropping can achieve higher productivity in both dry and wet conditions. Therefore, considering the fluctuating rainfall events in the future, it might be useful to alter the proportions of intercropped forage species in an artificial grassland to obtain optimal productivity by reducing the limitations of nitrogen availability. However, the economic viability of intercropping M. sativa and E. nutans should be evaluated in the future.

A Neuromorphic Processing System With Spike-Driven SNN Processor for Wearable ECG Classification.

This paper presents a neuromorphic processing system with a spike-driven spiking neural network (SNN) processor design for always-on wearable electrocardiogram (ECG) classification. In the proposed system, the ECG signal is captured by level crossing (LC) sampling, achieving native temporal coding with single-bit data representation, which is directly fed into an SNN in an event-driven manner. A hardware-aware spatio-temporal backpropagation (STBP) is suggested as the training scheme to adapt to the LC-based data representation and to generate lightweight SNN models. Such a training scheme diminishes the firing rate of the network with little plenty of classification accuracy loss, thus reducing the switching activity of the circuits for low-power operation. A specialized SNN processor is designed with the spike-driven processing flow and hierarchical memory access scheme. Validated with field programmable gate arrays (FPGA) and evaluated in 40 nm CMOS technology for application-specific integrated circuit (ASIC) design, the SNN processor can achieve 98.22% classification accuracy on the MIT-BIH database for 5-category classification, with an energy efficiency of 0.75 µJ/classification.

Grazing Changed Plant Community Composition and Reduced Stochasticity of Soil Microbial Community Assembly of Alpine Grasslands on the Qinghai-Tibetan Plateau.

Grazing is a substantial threat to the sustainability of grassland ecosystems, while it is uncertain about the variety of plant and soil microbial community and the linkages between them limit the comprehensive understanding of grazing ecology. We conducted an experiment on the effects of the grazing regimes rotational grazing (RG), continuous grazing (CG), and grazing exclusion (GE) on an alpine meadow in Qinghai-Tibetan Plateau. The differences of plant community composition, soil microbial community assembly mechanism, and taxonomic and functional composition between grazing regimes were examined, and the relationship between plant species and the soil microbes was assessed by constructing a co-occurrence network. The results showed that the plant community composition varied with the grazing regimes, while the soil microbial community composition did not vary with the grazing regimes. The soil bacterial functional composition was similar under RG and CG, while the soil fungal functional composition was similar under GE and RG. The soil microbial community under all grazing regimes was assembled mainly according to stochastic rather than deterministic mechanisms, and RG and CG reduced the relative importance of the stochastic ratio. At the microbial phylum level, CG and GE increased the relative abundance of Acidobacteria and Armatimonadetes and CG and RG increased the relative abundance of Elusimicrobia. In the network of plant species and soil microbial classes, plants and bacteria themselves were mainly positively linked (symbiosis and promotion), while plants and soil microbes were mainly negatively linked (competition). There were five microbial generalists in the network, which connected with many microbes, and four showed no difference in their abundance among the grazing regimes. Overall, the stable key microbes in the network and the fact that many of the plants are unconnected with microbes weakened the impact of grazing-induced changes in the plant community on soil microbes, probably resulting in the stable soil microbial community composition. Moreover, there was still a dominant and tolerant plant species, Kobresia pygmaea, that connected the plant and microbial communities, implying that the dominant plant species not only played a crucial role in the plant community but also acted as a bridge between the plants and soil microbes; thus, its tolerance and dominance might stabilize the soil microbial community.

Warming and spring precipitation addition change plant growth pattern but have minor effects on growing season mean gross ecosystem productivity in an alpine meadow.

Gross ecosystem productivity (GEP) plays an important role in global carbon cycling. However, how plant phenology and growth rate regulate GEP under climate change is unclear. Based on an in situ manipulative experiment using open top chambers from 2015 to 2018, we measured whole year warming and spring precipitation addition effects on plant phenology, plant growth rate and GEP. Our results showed that warming delayed plant green up (4 days) and withering (5 days), while spring precipitation addition advanced green up 13 days and did not change withering. Warming delayed the timing of the fast-growing phase 7 days, shortened length of the fast-growing phase 7 days and marginally increased the growth rate. Spring precipitation addition advanced the timing of the fast-growing phase 6 days, but did not change the length of the fast-growing phase or the growth rate. Both whole year warming and spring precipitation addition have not significantly affected growing season mean GEP. GEP is positively correlated with plant growth rate and negatively correlated with the length of the fast-growing phase. We provide an evidence that although warming did not change growing season mean productivity, it delayed plant fast-growing phase. Our findings suggest that management approaches for increasing water availability before the fast-growing phase should be intensified to increase ecosystem carbon uptake and grass supply for animal husbandry in spring.

Nitrogen Addition Affects Ecosystem Carbon Exchange by Regulating Plant Community Assembly and Altering Soil Properties in an Alpine Meadow on the Qinghai-Tibetan Plateau.

Nitrogen (N) deposition can affect the global ecosystem carbon balance. However, how plant community assembly regulates the ecosystem carbon exchange in response to the N deposition remains largely unclear, especially in alpine meadows. In this study, we conducted a manipulative experiment to examine the impacts of N (ammonium nitrate) addition on ecosystem carbon dioxide (CO2) exchange by changing the plant community assembly and soil properties at an alpine meadow site on the Qinghai-Tibetan Plateau from 2014 to 2018. The N-addition treatments were N0, N7, N20, and N40 (0, 7, 20, and 40 kg N ha-1year-1) during the plant growing season. The net ecosystem CO2 exchange (NEE), gross ecosystem productivity (GEP), and ecosystem respiration (ER) were measured by a static chamber method. Our results showed that the growing-season NEE, ER and GEP increased gradually over time with increasing N-addition rates. On average, the NEE increased significantly by 55.6 and 65.2% in N20 and N40, respectively (p < 0.05). Nitrogen addition also increased forage grass biomass (GB, including sedge and Gramineae) by 74.3 and 122.9% and forb biomass (FB) by 73.4 and 51.4% in N20 and N40, respectively (p < 0.05). There were positive correlations between CO2 fluxes (NEE and GEP) and GB (p < 0.01), and the ER was positively correlated with functional group biomass (GB and FB) and soil available N content (NO3 --N and NH4 +-N) (p < 0.01). The N-induced shift in the plant community assembly was primarily responsible for the increase in NEE. The increase in GB mainly contributed to the N stimulation of NEE, and FB and the soil available N content had positive effects on ER in response to N addition. Our results highlight that the plant community assembly is critical in regulating the ecosystem carbon exchange response to the N deposition in alpine ecosystems.

Effects of regional transport from different potential pollution areas on volatile organic compounds (VOCs) in Northern Beijing during non-heating and heating periods.

Despite the adoption of air quality control measures, the influence of regional transport on volatile organic compounds (VOCs) pollution has gradually increased in Beijing. In this study, the whole observation period (September 24 to December 12, 2020) was divided into heating period and non-heating period to explore the impact of changing VOCs sources in different observation periods, and also classified into "Type-N" and "Type-S" periods both in non-heating period and heating period to explore the impact of regional transport from the northern and southern regions of sampling site, respectively. The average VOCs concentrations in northern Beijing during observation period were 22.6 ± 12.6 ppbv, which showed a decrease trend in recent years compared with other studies. And higher VOCs concentrations were observed in Type-S than in Type-N period. The positive matrix factorization results showed that vehicular exhaust dominated VOCs (26.1%-33.7%), but coal combustion could not be ignored in heating period, when it was twice that in non-heating period. In particular, coal combustion dominated VOCs in southern trajectories (30.9%) in heating period. The analysis of concentration weighted trajectory showed that coal combustion was affected by regional transport from the southeast regions of Beijing, while vehicular exhaust was affected by urban and the southeast regions of Beijing. Regarding human health risks, the carcinogenic risks of benzene and ethylbenzene exceeded the acceptable cancer risk value (1 × 10-6), and were higher in Type-S than in Type-N period. The results indicated that regional transport from urban areas and the areas south of Beijing had a significant impact on VOCs in northern Beijing. Thus, targeted control measures for different potential pollution regions are important for controlling VOCs pollution in Beijing.

Backpropagation With Sparsity Regularization for Spiking Neural Network Learning.

The spiking neural network (SNN) is a possible pathway for low-power and energy-efficient processing and computing exploiting spiking-driven and sparsity features of biological systems. This article proposes a sparsity-driven SNN learning algorithm, namely backpropagation with sparsity regularization (BPSR), aiming to achieve improved spiking and synaptic sparsity. Backpropagation incorporating spiking regularization is utilized to minimize the spiking firing rate with guaranteed accuracy. Backpropagation realizes the temporal information capture and extends to the spiking recurrent layer to support brain-like structure learning. The rewiring mechanism with synaptic regularization is suggested to further mitigate the redundancy of the network structure. Rewiring based on weight and gradient regulates the pruning and growth of synapses. Experimental results demonstrate that the network learned by BPSR has synaptic sparsity and is highly similar to the biological system. It not only balances the accuracy and firing rate, but also facilitates SNN learning by suppressing the information redundancy. We evaluate the proposed BPSR on the visual dataset MNIST, N-MNIST, and CIFAR10, and further test it on the sensor dataset MIT-BIH and gas sensor. Results bespeak that our algorithm achieves comparable or superior accuracy compared to related works, with sparse spikes and synapses.