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Extensive spatiotemporal analyses of long-trend surface ozone in the Yangtze River Delta (YRD) region and its meteorology-related and emission-related have not been systematically analyzed. In this study, by using 8-year-long (2015-2022) surface ozone observation data, we attempted to reveal the variation of multiple timescale components using the Kolmogorov-Zurbenko filter, and the effects of meteorology and emissions were quantitatively isolated using multiple linear regression with meteorological variables. The results showed that the short-term, seasonal, and long-term components accounted for daily maximum 8-hr average O3 (O3-8 hr) concentration, 46.4%, 45.9%, and 1.0%, respectively. The meteorological impacts account for an average of 71.8% of O3-8 hr, and the YRD's eastern and northern sections are meteorology-sensitive areas. Based on statistical analysis technology with empirical orthogonal function, the contribution of meteorology, local emission, and transport in the long-term component of O3-8 hr were 0.21%, 0.12%, and 0.6%, respectively. The spatiotemporal analysis indicated that a distinct decreasing spatial pattern could be observed from coastal cities towards the northwest, influenced by the monsoon and synoptic conditions. The central urban agglomeration north and south of the YRD was particularly susceptible to local pollution. Among the cities studied, Shanghai, Anqing, and Xuancheng, located at similar latitudes, were significantly impacted by atmospheric transmission-the contribution of Shanghai, the maximum accounting for 3.6%.

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Sedimentation sludge water (SSW), a prominent constituent of wastewater from drinking water treatment plants, has received limited attention in terms of its treatment and utilization likely due to the perceived difficulties associated with managing SSW sludge. This study comprehensively evaluated the water quality of SSW by comparing it to a well-documented wastewater (filter backwash water (FBW)). Furthermore, it investigated the pollutant variations in the SSW during pre-sedimentation process, probed the underlying reaction mechanism, and explored the feasibility of employing a pilot-scale coagulation-sedimentation process for SSW treatment. The levels of most water quality parameters were generally comparable between SSW and FBW. During the pre-sedimentation of SSW, significant removal of turbidity, bacterial counts, and dissolved organic matter (DOM) was observed. The characterization of DOM components, molecular weight distributions, and optical properties revealed that the macromolecular proteinaceous biopolymers and humic acids were preferentially removed. The characterization of particulates indicated that high surface energy, zeta potential, and bridging/adsorption/sedimentation/coagulation capacities in aluminum residuals of SSW, underscoring its potential as a coagulant and promoting the generation and sedimentation of inorganic-organic complexes. The coagulation-sedimentation process could effectively remove pollutants from low-turbidity SSW ([turbidity]0 < 15 NTU). These findings provide valuable insights into the water quality dynamics of SSW during the pre-sedimentation process, facilitating the development of SSW quality management and enhancing its reuse rate.

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The electrokinetic (EK) process has been proposed for soil decontamination from heavy metals and organic matter. The advantages of the EK process include the low operating energy, suitability for fine-grained soil decontamination, and no need for excavation. During the last three decades, enhanced and hybrid EK systems were developed and tested for improving the efficiency of contaminants removal from soils. Chemically enhanced-EK processes exhibited excellent efficiency in removing contaminants by controlling the soil pH or the chemical reaction of contaminants. EK hybrid systems were tested to overcome environmental hurdles or technical drawbacks of decontamination technologies. Hybridization of the EK process with phytoremediation, bioremediation, or reactive filter media (RFM) improved the remediation process performance by capturing contaminants or facilitating biological agents' movement in the soil. Also, EK process coupling with solar energy was proposed to treat off-grid contaminated soils or reduce the EK energy requirements. This study reviews recent advancements in the enhancement and hybrid EK systems for soil remediation and the type of contaminants targeted by the process. The study also covered the impact of operating parameters, imperfect pollution separation, and differences in the physicochemical characteristics and microstructure of soil/sediment on the EK performance. Finally, a comparison between various remediation processes was presented to highlight the pros and cons of these technologies.

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Existing medical image segmentation methods may only consider feature extraction and information processing in spatial domain, or lack the design of interaction between frequency information and spatial information, or ignore the semantic gaps between shallow and deep features, and lead to inaccurate segmentation results. Therefore, in this paper, we propose a novel frequency selection segmentation network (FSSN), which achieves more accurate lesion segmentation by fusing local spatial features and global frequency information, better design of feature interactions, and suppressing low correlation frequency components for mitigating semantic gaps. Firstly, we propose a global-local feature aggregation module (GLAM) to simultaneously capture multi-scale local features in the spatial domain and exploits global frequency information in the frequency domain, and achieves complementary fusion of local details features and global frequency information. Secondly, we propose a feature filter module (FFM) to mitigate semantic gaps when we conduct cross-level features fusion, and makes FSSN discriminatively determine which frequency information should be preserved for accurate lesion segmentation. Finally, in order to make better use of local information, especially the boundary of lesion region, we employ deformable convolution (DC) to extract pertinent features in the local range, and makes our FSSN can focus on relevant image contents better. Extensive experiments on two public benchmark datasets show that compared with representative medical image segmentation methods, our FSSN can obtain more accurate lesion segmentation results in terms of both objective evaluation indicators and subjective visual effects with fewer parameters and lower computational complexity.

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This study compares how a modified distributed Bragg reflector (DBR) and yellow color filter (Y-CF) increase the color purity, viewing angle, and brightness of the quantum dot color conversion layer (QDCC) for micro-LED displays. We designed and built a 53-layer high-performance modified DBR with almost total blue leakage filtering (T %: 0.16 %) and very high G/R band transmittance (T %: 96.97 %) for comparison. We also use a Y-CF that filters blue light (T %: 0.84 %) and has good G/R band transmittance (T %: 94.83 %). Due to DBR's angle dependency effect, the modified DBR/QDCC structure offers a remarkable color gamut (117.41 % NTSC) at the forward viewing angle, but this rapidly diminishes beyond 30°. The Y-CF/QDCC structure retains 116 % NTSC color at all viewing angles. Because of its consistent color performance at all viewing angles, sufficient brightness, and outstanding color gamut, the Y-CF/QDCC structure is the best option for contemporary QDCC-based micro-LED displays.

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Based on novel phosphorus-doped carbon dots (PCDs), a simple, quick, and accurate fluorescence probe for sarecycline (SAR) determination has been created. The PCDs were prepared in just five minutes using green, straightforward one-step microwave pyrolysis. To create the PCD probe, sodium phosphate monobasic was utilized as a phosphorus dopant and citric acid as a carbon supply. The proposed synthesis method was energy efficient and yielded CDs with a narrow particle size distribution. Based on inner-filter effect mechanism, the generated PCDs were used as nano-probe for SAR determination. The fluorescence quenching intensity showed a strong linear relationship with SAR concentration in the 3-90-µM range with a detection limit of 0.88 µM. Because there is no surface alteration of the CDs or creation of a covalent bond between SAR and PCDs, the developed approach is quick, easy, inexpensive, and requires less time. The new probe's enhanced sensitivity, broad linear range, and acceptable selectivity made it suitable for SAR measurement in pharmaceutical formulations and spiked human plasma. Most importantly, the Green Analytical Procedure Index (GAPI) and Analytical GREEnness (AGREE) assessments showed that the suggested method was environmentally friendly.

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Measurements of the refractive index of liquids are in high demand in numerous fields such as agriculture, food and beverages, and medicine. However, conventional ellipsometric refractive index measurements are too expensive and labor-intensive for consumer devices, while Abbe refractometry is limited to the measurement at a single wavelength. Here, a new approach is proposed using machine learning to unlock the potential of colorimetric metasurfaces for the real-time measurement of the dispersive refractive index of liquids over the entire visible spectrum. The platform with a proof-of-concept experiment for measuring the concentration of glucose is further demonstrated, which holds a profound impact in non-invasive medical sensing. High-index-dielectric metasurfaces are designed and fabricated, while their experimentally measured reflectance and reflected colors, through microscopy and a standard smartphone, are used to train deep-learning models to provide measurements of the dispersive background refractive index with a resolution of ≈10-4, which is comparable to the known index as measured with ellipsometry. These results show the potential of enabling the unique optical properties of metasurfaces with machine learning to create a platform for the quick, simple, and high-resolution measurement of the dispersive refractive index of liquids, without the need for highly specialized experts and optical procedures.

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Certain aerobic bacteria produce bacterial cellulose (BC) to protect themselves from UV radiation. Inspired by this natural function, the UV-filtering capacity of wet BC film (BC) and dried BC (BC-Dried) is evaluated and it is concluded that both samples hardly filter UVA, but filter UVB to some extent, especially BC-Dried. Moreover, this filtering capacity does not diminish but significantly increases with time, with efficiencies in the 145-160 min time range equal to or greater than most UV filters of the market. This increase in efficiency is due to the fact that the BC structure is modified by prolonged exposure to UVB radiation. Specifically, UVB causes sintering of the cellulose fibers, making the structure denser and increasing its reflection and scattering of UVB radiation. Remarkably, this UVB filtering ability of BC allows it to protect key skin probiotics, Lactobacillus fermentum (L. fermentum) and Cutibacterium acnes (C. acnes), against UVB damage. While the protection of healthy skin microbiota is not currently a regulatory requirement for sunscreens with UV filters, it may become a key differentiator for future UV filters given the increasing evidence on the role of skin microbiota in health.

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We have designed and constructed a low-cost Wien filter based on strong permanent magnets and integrated it into an ion soft-landing instrument to enable parallel deposition as well as one- and two-dimensional surface patterning of mass-selected ions using dynamic fields. We show the capabilities of this device for separating ions from a multicomponent high-flux continuous ion beam and simultaneous deposition of ions of different mass-to-charge ratios onto discrete locations on a surface. When a dynamic electric field is applied parallel to the magnetic field, ions are deposited in one-dimensional arrays, laterally separated by mass. The field's strength, frequency, and waveform type determine both the lengths of the arrays and the density of ions across the 1-D pattern. Additionally, a second dynamic field from user-defined waveforms orthogonal to the magnetic field enables two-dimensional surface patterning of ions while maintaining mass separation. These experiments demonstrate the practical utility of a Wien filter for the controlled fabrication of interfaces with arbitrary patterns of mass-selected ions.

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In this research work, Semantic-Preserved Generative Adversarial Network optimized by Piranha Foraging Optimization for Thyroid Nodule Classification in Ultrasound Images (SPGAN-PFO-TNC-UI) is proposed. Initially, ultrasound images are gathered from the DDTI dataset. Then the input image is sent to the pre-processing step. During pre-processing stage, the Multi-Window Savitzky-Golay Filter (MWSGF) is employed to reduce the noise and improve the quality of the ultrasound (US) images. The pre-processed output is supplied to the Generalized Intuitionistic Fuzzy C-Means Clustering (GIFCMC). Here, the ultrasound image's Region of Interest (ROI) is segmented. The segmentation output is supplied to the Fully Numerical Laplace Transform (FNLT) to extract the features, such as geometric features like solidity, orientation, roundness, main axis length, minor axis length, bounding box, convex area, and morphological features, like area, perimeter, aspect ratio, and AP ratio. The Semantic-Preserved Generative Adversarial Network (SPGAN) separates the image as benign or malignant nodules. Generally, SPGAN does not express any optimization adaptation methodologies for determining the best parameters to ensure the accurate classification of thyroid nodules. Therefore, the Piranha Foraging Optimization (PFO) algorithm is proposed to improve the SPGAN classifier and accurately identify the thyroid nodules. The metrics, like F-score, accuracy, error rate, precision, sensitivity, specificity, ROC, computing time is examined. The proposed SPGAN-PFO-TNC-UI method attains 30.54%, 21.30%, 27.40%, and 18.92% higher precision and 26.97%, 20.41%, 15.09%, and 18.27% lower error rate compared with existing techniques, like Thyroid detection and classification using DNN with Hybrid Meta-Heuristic and LSTM (TD-DL-HMH-LSTM), Quantum-Inspired convolutional neural networks for optimized thyroid nodule categorization (QCNN-OTNC), Thyroid nodules classification under Follow the Regularized Leader Optimization based Deep Neural Networks (CTN-FRL-DNN), Automatic classification of ultrasound thyroids images using vision transformers and generative adversarial networks (ACUTI-VT-GAN) respectively.

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BACKGROUND: Curcumin has been used in traditional medicine because of its pharmacological activity, including antioxidant, antibacterial, anticancer, and anticarcinogenic properties. Therefore, sensitive and selective monitoring of curcumin is highly demand for practical application. RESULTS: In this study, we describe the construction of a fluorescence method for curcumin assay based on nitrogen-doped MoS2 quantum dots (N-MoS2 QDs). The N-MoS2 QDs are constructed by a solvothermal method using sodium molybdate and Cys as precursors. With the addition of curcumin, the bright blue fluorescence of N-MoS2 QDs is quenched by the inner filter effect (IFE). The QDs emitted bright blue fluorescence and could be quenched by the addition of curcumin via IFE. The dynamic range is the range of 0.1-10 µM for curcumin detection, with a detection limit of 59 nM. N-MoS2 QDs were applied for curcumin assay in real samples with good recovery. In addition, the N-MoS2 QDs exhibited relative low cytotoxicity and could be applied for fluorescence-based imaging in biological samples. SIGNIFICANCE: Our study indicates that the sensor possesses good selectivity to monitor curcumin in water samples, human urine samples, ginger powder samples, mustard samples, and curry samples with satisfactory recoveries. The N-MoS2 QDs possess less cytotoxicity with excellent biocompatibility and were applied for in vitro cell imaging.

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PURPOSE: In this study, we evaluated image quality and radiation dose reduction when a Copper (Cu) filter was added to hip joint X-ray imaging. METHODS: We measured effective energy without (0 mm) and with (0.1/0.2 mm) Cu-added filter at 70 kV, and we calculated soft tissue-bone contrast and signal-difference-to-noise-ratio (SDNR) under constant entrance surface dose. After that, we estimated the dose reduction rate. RESULTS: The effective energy was 32.07 keV for 0 mm Cu, 37.59 keV for 0.1 mm Cu, and 40.91 keV for 0.2 mm Cu. As the thickness of the Cu-added filter was increased, contrast decreased, but SDNR increased. The dose reduction rate in bone calculated measuring SDNR was 34% for 0.1 mm Cu and 47% for 0.2 mm Cu in max. CONCLUSION: It was suggested that adding Cu filter to hip-joint X-ray imaging could reduce entrance surface dose while maintaining the image quality based on SDNR.

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The high prevalence of acute kidney injury (AKI) in ICU patients emphasizes the need to understand factors influencing continuous renal replacement therapy (CRRT) circuit lifespan for optimal outcomes. This review examines key pharmacological interventions-citrate (especially in regional citrate anticoagulation), unfractionated heparin (UFH), low molecular weight heparin (LMWH), and nafamostat mesylate (NM)-and their effects on filter longevity. Citrate shows efficacy with lower bleeding risks, while UFH remains cost-effective, particularly in COVID-19 cases. LMWH is effective but associated with higher bleeding risks. NM is promising for high-bleeding risk scenarios. The review advocates for non-tunneled, non-cuffed temporary catheters, especially bedside-inserted ones, and discusses the advantages of surface-modified dual-lumen catheters. Material composition, such as polysulfone membranes, impacts filter lifespan. The choice of treatment modality, such as Continuous Veno-Venous Hemodialysis (CVVHD) or Continuous Veno-Venous Hemofiltration with Dialysis (CVVHDF), along with the management of effluent volume, blood flow rates, and downtime, are critical in prolonging filter longevity in CRRT. Patient-specific conditions, particularly the type of underlying disease, and the implementation of early mobilization strategies during CRRT are identified as influential factors that can extend the lifespan of CRRT filters. In conclusion, this review offers insights into factors influencing CRRT circuit longevity, supporting evidence-based practices and suggesting further multicenter studies to guide ICU clinical decisions.

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MOTIVATION: Structural variants (SVs) play an important role in genetic research and precision medicine. As existing SV detection methods usually contain a substantial number of false positive calls, approaches to filter the detection results are needed. RESULT: We developed a novel deep learning-based SV filtering tool, CSV-Filter, for both short and long reads. CSV-Filter uses a novel multi-level grayscale image encoding method based on CIGAR strings of the alignment results and employs image augmentation techniques to improve SV feature extraction. CSV-Filter also utilizes self-supervised learning networks for transfer as classification models, and employs mixed-precision operations to accelerate training. The experiments showed that the integration of CSV-Filter with popular SV detection tools could considerably reduce false positive SVs for short and long reads, while maintaining true positive SVs almost unchanged. Compared with DeepSVFilter, a SV filtering tool for short reads, CSV-Filter could recognize more false positive calls and support long reads as an additional feature. AVAILABILITY AND IMPLEMENTATION: https://github.com/xzyschumacher/CSV-Filter.

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Heavy metals occur naturally in the environment, and their concentration varies in soil across different regions. However, the presence of heavy metals may influence the antimicrobial resistance (AMR) in bacterial populations. Therefore, the objective of this study was to investigate and characterise the antimicrobial resistance profiles of Enterobacterales in soil and bovine milk filters from high and low zinc-containing regions in Ireland. In total, 50 soil samples and 29 milk filters were collected from two geographic locations with varying soil zinc concentrations. Samples were cultured for the enumeration and detection of Enterobacterales. Specifically, extended-spectrum beta-lactamase-producing Enterobacterales, carbapenem-resistant Enterobacterales and ciprofloxacin-resistant Enterobacterales were isolated using selective media. Species identification was performed using MALDI-TOF. The phenotypic resistance profiles of selected Enterobacterales were determined by disk diffusion testing, following EUCAST and CLSI criteria; while, the genotypic resistance profiles of the same isolates were determined by whole genome sequencing (WGS). Heavy metal concentrations were also measured for all soil samples. A total of 40 antimicrobial resistant Enterobacterales were identified in soil (n = 31) and milk filters (n = 9). The predominant species detected in the high zinc-containing region was Escherichia coli in both sample types (soil n = 10, milk filters n = 2), while in the low zinc-containing region Serratia fonticola was predominant in soil samples (n = 8) and E. coli in milk filters (n = 4). Ten E. coli isolates identified from soil samples in the high zinc-containing region were multidrug resistant, showing resistance to all the antimicrobials tested, except for carbapenems. The WGS findings confirmed the phenotypic resistance results. Moreover, zinc resistance-associated genes and genes encoding for efflux pumps were identified. The current study revealed distinct phenotypic resistance profiles of Enterobacterales in low and high zinc-containing regions, and highlighted the benefit of utilising milk filters for AMR surveillance in dairy production.

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Existing remaining useful life (RUL) prediction methods considering multi-source variability were not applicable to the situation that the uneven measurement interval distribution and inconsistent measurement frequency of degrading equipment. This type of method also has ignored the variability of adaptive drift in the future degradation process. In view of this, based on adaptive Wiener process, the paper proposes a new nonlinear degradation method of the RUL prediction. Firstly, adopting the adaptive Wiener process, we have constructed the nonlinear degradation model with multi-source variability, which randomness of the parameters in the nonlinear function. Secondly, the real-time estimation of multiple hidden states can be realized by the particle filter algorithm. It has derived the RUL distribution in the sense of first hitting time. Using monitoring data of degrading equipment, the adaptive update of model parameters was implemented by expectation maximization algorithm. Finally, the effectiveness and superiority of the proposed model are validated through numerical simulation and lithium-ion battery experiments. The results show that it can effectively improve the prediction accuracy, which has potential application value.

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The timely and accurate acquisition of crop-growth information is a prerequisite for implementing intelligent crop-growth management, and portable multispectral imaging devices offer reliable tools for monitoring field-scale crop growth. To meet the demand for obtaining crop spectra information over a wide band range and to achieve the real-time interpretation of multiple growth characteristics, we developed a novel portable snapshot multispectral imaging crop-growth sensor (PSMICGS) based on the spectral sensing of crop growth. A wide-band co-optical path imaging system utilizing mosaic filter spectroscopy combined with dichroic mirror beam separation is designed to acquire crop spectra information over a wide band range and enhance the device's portability and integration. Additionally, a sensor information and crop growth monitoring model, coupled with a processor system based on an embedded control module, is developed to enable the real-time interpretation of the aboveground biomass (AGB) and leaf area index (LAI) of rice and wheat. Field experiments showed that the prediction models for rice AGB and LAI, constructed using the PSMICGS, had determination coefficients (R²) of 0.7 and root mean square error (RMSE) values of 1.611 t/ha and 1.051, respectively. For wheat, the AGB and LAI prediction models had R² values of 0.72 and 0.76, respectively, and RMSE values of 1.711 t/ha and 0.773, respectively. In summary, this research provides a foundational tool for monitoring field-scale crop growth, which is important for promoting high-quality and high-yield crops.

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Microplastics (MPs) contamination in marine organisms is a significant threat to seafood consumers worldwide. This study is the first to investigate the abundance of MPs in the commercial bivalves from six sites along Thailand's coastline, the daily exposure of bivalve consumers to MPs, and potential associated health risks. The microplastic occurrence varied from 69 % to 93 % in four bivalve species while the average abundance of MPs was 1.87 ± 0.86 items/individual or 0.46 ± 0.43 items/g ww. Benthic bivalves (cockles and clams) contained more MPs than their pelagic counterparts (mussels and oysters). Small blue microfibers (<500 µm) were the most abundant. The most common polymers were natural based polymers (cotton and rayon) and polyethylene terephthalate (PET). The daily microplastic exposure for consumers was 0.52 items/person. Although the risk of microplastic contamination is low, we recommend investigation into the transfer of MPs within the food web, notably as it may pose significant human health concerns.

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In diagnostic radiology, the air kerma is an essential parameter. Radiologists consider the air kerma, when calculating organ doses and dangers to patients. The intensity of the radiation beam is represented by the air kerma, which is the value of energy wasted by a photon as it travels through air. Because of the heel effect in X-ray sources, air kerma varies throughout the field of medical imaging systems. One possible contributor to this discrepancy is the X-ray tube's voltage. In this study, an approach has been proposed for predicting the air kerma anywhere inside the field of X-ray beams utilized in medical diagnostic imaging systems. As a first step, a diagnostic imaging system was modelled using the Monte Carlo N-Particle platform. We used a tungsten target and aluminum and beryllium filters of varying thicknesses to recreate the X-ray tube. The air kerma has been measured in different parts of the conical X-ray beam that is working at 30, 50, 70, 90, 110, 130, and 150 kV. This gives enough data for training neural networks. The voltage of the X-ray tube, filter type, filter thickness, and the coordinates of each point used to calculate the air kerma were all inputs to the MLP neural network. The MLP architecture, known for its significant advancements in research and expanding applications, was trained to predict the quantity of air kerma as its output. Specifically, by considering X-ray tube filters of varying thicknesses, the trained MLP model demonstrated its capability to accurately predict the air kerma at every point within the X-ray field for a range of X-ray tube voltages typically used in medical diagnostic radiography (30-150 kV).

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Fine particulate matter (PM2.5) constituents are greatly affected by site-specific emission sources and are one of the main reasons for oxidative stress that leads to cardiovascular ailments. This study investigated the temporal, seasonal, and episodic variations in the oxidative potential (OP) of PM2.5 and its association with chemical components. Additionally, we have also examined the effect of filter substrates on OP. Dithiothreitol (DTT) and ascorbic acid (AA) acellular assays were used to estimate the formation of reactive oxygen species (ROS) in PM2.5 samples collected over a year from a regional site in India. PM2.5 morphology and functional groups were also analyzed. Results showed that OPDTTv was at the highest in winter (2.56 ±â€¯0.84 nmol min-1 m-3) and at the lowest during monsoon (0.79 ±â€¯0.65 nmol min-1 m-3). OPAAv exhibited the highest activity in post-monsoon (0.09 ±â€¯0.04 nmol min-1 m-3) and least in summer (0.05 ±â€¯0.04 nmol min-1 m-3). Biomass burning (BB) and open-field burning of crop residue during the rabi and kharif harvesting seasons were associated with significantly elevated PM2.5 toxicity, which is indicative of the contribution of combustion-derived particles. OPDTTv and OPAAv levels from BB in post-monsoon were 21 % and 67 % higher than the levels observed during BB in summer. Flaky irregular agglomerates and porous structures were observed during the BB period. Fourier-transformed infrared spectroscopy revealed that traffic-emitted organic hydrocarbons CH functional group was dominant across the season. Further, chemical species such as organics (OC and EC fractions) and ions (SO42-, NH4+, Cl-, NO3-) were found to be significantly associated with OP. Among the three filter substrates, the Teflon showed higher OP variability for both assays. This study emphasizes the impact of regional toxic aerosols across seasons and during episodic events. It contributes to our understanding of the toxicity of ambient PM2.5, which is crucial for developing targeted air-quality management strategies.