Resonant-mode engineering for additive reflective structural colors with high brightness and high color purity.

We present quad-layered reflective structural color filters generating vivid additive primary colors by controlling a mode number in a Fabry-Perot (FP) cavity and an anti-reflective (AR) coating layer, thus accomplishing high spectral contrast which is highly demanded in creating sharp colors. The reflection brightness of fabricated structural color filters is over 78% and a color gamut is comparable to the standard color gamut (sRGB). Higher-order resonant modes are exploited yielding a narrow passband with strong suppression of the reflection at shorter and longer wavelength ranges for a green color, while red and blue colors are produced by employing fundamental resonant modes. Besides, the structural color filters maintain both high brightness and high color purity at oblique incidence angles up to 40° due to a small angle of refraction by a cavity medium with high refractive index. Moreover, a large-scale fabrication is enabled owing to the simplicity of a device structure, where thin film deposition is used. The scheme presented in this work may open the door to a number of applications, such as reflective displays, imaging devices, colored photovoltaics, and decorations.

International assessment results on non-NIOSH approved respirators by the national personal protective technology laboratory (NPPTL): a data note.

OBJECTIVES: Due to the COVID-19 pandemic and the shortage of the National Institute for Occupational Safety & Health (NIOSH)-approved N95 respirators, the Food and Drug Administration granted an Emergency Use Authorization to allow the use of non-NIOSH approved respirators provided that these respirators must undergo tests by a protocol of TEB-APR-STP-0059, similar methods of NIOSH standard testing procedure. This initiative safeguards the quality of respirators and the effectiveness of occupational protection. The dataset of all the testing results could benefit further analysis of COVID-19 infection rates in relation to different types of N95 respirators used and identify potential correlations of various test parameters in the testing system for validation. The analysis enhances understanding of the quality, effectiveness, and performance of N95 respirators in the prevention of respiratory infectious transmission and develops improved occupational safety measures. DATA DESCRIPTION: The dataset was transformed, transcribed, and compiled from the official testing data of non-NIOSH-approved N95 respirators reported in the NIOSH website under the Centers for the Disease Control and Prevention in the United States. The dataset included details of 7,413 testing results of N95 respirators (manufacturer, model, and maximum and minimum filtration efficiency) and test parameters (flow rate, initial filter resistance, and initial percent leakage). Supplementary items were added to increase the availability of data analysis and enhance the interpretability of the assessments of the quality of N95 respirators.

Assessing eDNA capture method from aquatic environment to optimise recovery of human mt-eDNA.

Previous studies have shown that environmental DNA (eDNA) from human sources can be recovered from natural bodies of water, and the generation of DNA profiles from such environmental samples may assist in forensic investigations. However, fundamental knowledge gaps exist around the factors influencing the probability of detecting human eDNA and the design of optimal sampling protocols. One of these is understanding the particle sizes eDNA signals are most strongly associated with and the most appropriate filter size needed for efficiently capturing eDNA particles. This study assessed the amount of mitochondrial eDNA associated with different particle sizes from human blood and skin cells recovered from freshwater samples. Samples (300 mL) were taken from experimental 10 L tanks of freshwater spiked with 50 µL of human blood or skin cells deposited by vigorously rubbing hands together for two minutes in freshwater. Subsamples were collected by passing 250 mL of experimental water sample through six different filter pore sizes (from 0.1 to 8 µm). This process was repeated at four time intervals after spiking over 72 hours to assess if the particle size of the amount of eDNA recovered changes as the eDNA degrades. Using a human-specific quantitative polymerase chain reaction (qPCR) assay targeting the HV1 mitochondrial gene region, the total amount of mitochondrial eDNA associated with different particle size fractions was determined. In the case of human blood, at 0 h, the 0.45 µm filter pore size captured the greatest amount of mitochondrial eDNA, capturing 42 % of the eDNA detected. The pattern then changed after 48 h, with the 5 µm filter pore size capturing the greatest amount of eDNA (67 %), and 81 % of eDNA at 72 h. Notably, a ten-fold dilution proved to be a valuable strategy for enhancing eDNA recovery from the 8 µm filter at all time points, primarily due to the PCR inhibition observed in hemoglobin. For human skin cells, the greatest amounts of eDNA were recovered from the 8 µm filter pore size and were consistent through time (capturing 37 %, 56 %, and 88 % of eDNA at 0 hours, 48 hours, and 72 hours respectively). There is a clear variation in the amount of eDNA recovered between different cell types, and in some forensic scenarios, there is likely to be a mix of cell types present. These results suggest it would be best to use a 5 µm filter pore size to capture human blood and an 8 µm filter pore size to capture human skin cells to maximize DNA recovery from freshwater samples. Depending on the cell type contributing to the eDNA, a combination of different filter pore sizes may be employed to optimize the recovery of human DNA from water samples. This study provides the groundwork for optimizing a strategy for the efficient recovery of human eDNA from aquatic environments, paving the way for its broader application in forensic and environmental sciences.

Microplastics determination and quantification in two benthic filter feeders Sabella spallanzanii, Polychaeta and Paraleucilla magna, Porifera.

Plastic pollution is a worldwide problem especially in the marine environment. Plastic items once fragmented into microplastics (MPs), can be captured by different marine species. Benthic filter feeders like sponges and polychaetas, due to their trophic strategy, are highly exposed to MPs pollution. Herein a simple but effective method to digest the fan worm Sabella spallanzanii and the calcareous sponge Paraleucilla magna is presented: a solution with KOH and H2O2 was able to remove quantitatively (more than 98 %) the organic matter in 3 h while an acid treatment dissolved most of spicules and chaetes in less than 30 min. MPs were easily identified both microscopically and spectroscopically on filters. Quantification in animals collected from the same environment showed that, on average, sponges accumulate fewer MPs than polychaetes (66 ± 31 and 117 ± 46 particles/g dry weight, respectively). The plastic recovery of the method was validated using three different approaches (spiking of standard PS microspheres, of common-use plastic objects, and of microplastics already weathered in marine environment). This procedure can make it easier and cost-effective to process biota in monitoring studies, providing information about bioindicator/bioremediation species.

Effectual seizure detection using MBBF-GPSO with CNN network.

EEG is the most common test for diagnosing a seizure, where it presents information about the electrical activity of the brain. Automatic Seizure detection is one of the challenging tasks due to limitations of conventional methods with regard to inefficient feature selection, increased computational complexity and time and less accuracy. The situation calls for a practical framework to achieve better performance for detecting the seizure effectively. Hence, this study proposes modified Blackman bandpass filter-greedy particle swarm optimization (MBBF-GPSO) with convolutional neural network (CNN) for effective seizure detection. In this case, unwanted signals (noise) is eliminated by MBBF as it possess better ability in stopband attenuation, and, only the optimized features are selected using GPSO. For enhancing the efficacy of obtaining optimal solutions in GPSO, the time and frequency domain is extracted to complement it. Through this process, an optimized features are attained by MBBF-GPSO. Then, the CNN layer is employed for obtaining the productive classification output using the objective function. Here, CNN is employed due to its ability in automatically learning distinct features for individual class. Such advantages of the proposed system have made it explore better performance in seizure detection that is confirmed through performance and comparative analysis.

People see what they want to see: an EEG study.

This study explored selective exposure and confirmation bias in the choices participants made about which political videos to watch, and whether their political positions changed after they watched videos that either agreed with or opposed their positions on two controversial issues in South Korea: North Korea policy and social welfare policy. The participants completed questionnaires before and after they watched the videos, were asked to select thumbnails of videos before they watched any, and had their brain wave activity measured through electroencephalogram (EEG) as they watched both types of videos. The participants demonstrated selective exposure as they primarily selected video thumbnails with content that matched their political orientations, and they demonstrated confirmation bias as their questionnaire responses after they watched the videos indicated that their positions had hardened. There were also statistically significant differences in alpha, beta, sensory motor rhythm, low beta, mid beta, and fast alpha activity depending on the political orientation consistency between the participants and the videos. Future studies could expand this line of research beyond college students and beyond Asia, and longitudinal work could also be conducted to determine if the obtained patterns remain constant over time.

Dosimetric Evaluation of Semiflex Three-dimensional Chamber under Unflatten Beam in Comparison among Different Detectors.

Purpose: The goal of this study is to investigate the dosimetric properties of a Semiflex three-dimensional (3D) chamber in an unflatten beam and compare its data from a small to a large field flattening filter-free (FFF) beam with different radiation detectors. Methods: The sensitivity, linearity, reproducibility, dose rate dependency, and energy dependence of a Semiflex 3D detector in flattening filter and filter-free beam were fully investigated. The minimum radiation observed field widths for all detectors were calculated using lateral electronic charged particle equilibrium to investigate dosimetric characteristics such as percentage depth doses (PDDs), profiles, and output factors (OPFs) for Semiflex 3D detector under 6FFF Beam. The Semiflex 3D measured data were compared to that of other detectors employed in this study. Results: The ion chamber has a dosage linearity deviation of +1.2% for <10 MU, a dose-rate dependency deviation of +0.5%, and significantly poorer sensitivity due to its small volume. There is a difference in field sizes between manufacturer specs and derived field sizes. The measured PDD, profiles, and OPFs of the Semiflex 3D chamber were within 1% of each other for all square field sizes set under linac for the 6FFF beam. Conclusion: It was discovered to be an appropriate detector for relative dose measurements for 6 FFF beams with higher dose rates for field sizes more than or equal to 3 cm × 3 cm.

Efficient tensor decomposition-based filter pruning.

In this paper, we present CORING, which is short for effiCient tensOr decomposition-based filteR prunING, a novel filter pruning methodology for neural networks. CORING is crafted to achieve efficient tensor decomposition-based pruning, a stark departure from conventional approaches that rely on vectorized or matricized filter representations. Our approach represents a significant leap forward in the field by introducing tensor decompositions, specifically the HOSVD, which preserves the multidimensional nature of filters while providing a low-rank approximation, thus substantially reducing complexity. Furthermore, we introduce a versatile method for calculating filter similarity by using the low-rank approximation offered by the HOSVD. This obviates the need for using full filters or reshaped versions and enhances the overall efficiency and effectiveness of our approach. Extensive experimentation across diverse architectures and datasets spanning various vision tasks, including image classification, object detection, instance segmentation, and keypoint detection, validates CORING's prowess. Remarkably, it outperforms state-of-the-art methods in reducing MACs and parameters, consistently enhancing validation accuracy. Furthermore, we supplement our quantitative results with a comprehensive ablation study, providing substantial evidence of the efficiency of our tensor-based approach. Beyond quantitative outcomes, qualitative results vividly illustrate CORING's ability to retain essential features within pruned neural networks. Our code is available for research purposes.

Particle filter for fatigue crack growth prediction using SH0 wave on-line monitoring.

Fatigue crack is one of the main failure modes of pressure vessels. Online monitoring and predicting methods of crack growth play an important role in the operation of important pressure vessel. The SH0 wave is non-dispersive, and it is not disturbed by internal media of pressure vessel and very sensitive to cracks, therefore it is suitable for fatigue crack growth monitoring. Moreover, fatigue crack growth in industry is affected by material properties, loads, which usually shows some uncertainty. And the particle filter (PF) is well suited to deal with prediction problems affected by uncertainty. Hence, the prediction method of crack growth based on SH0 wave monitoring and PF is proposed (short for SH0-PF). The basic theory of crack monitoring method using SH0 wave is introduced, and the signal feature extraction using the damage index is studied. The state equation characterizing the fatigue crack growth is established by Paris model, and the observation equation is established based on the normalized correlation moment damage index according to monitoring signal using SH0 wave. The prediction reliability of the fatigue crack growth applying SH0-PF is verified by experiment with the single edge notched specimen. The experimental results indicate that the prediction accuracy of SH0-PF is better than that of the traditional Paris model.

Development of an "on-off-on" fluoroprobe utilizing an anthrylimidazole-based fluorescent ionic liquid for sensitive Cr(VI) and ascorbic acid detection.

Developing a rapid detection method of Cr(VI) and ascorbic acid (AA) is vital in the food and environmental fields. Herein, an anthrylimidazole-based fluorescent ionic liquid (AI-FIL) with the advantageous fluorescent properties was successfully prepared and used to construct a promising "on-off-on" fluoroprobe for rapid/sensitive Cr(VI) and AA detection. Cr(VI) could effectively quench the fluorescence of AI-FIL owing to the inner-filter effect and photoinduced electron-transfer process. However, the decreased fluorescence could be rapidly recovered by AA owing to the redox reaction between AA and Cr(VI). For Cr(VI) detection, a satisfactorily linear response (0.03-300 µM) was achieved with the corresponding detection limit of 9 nM. For AA detection, a good linearity from 1 to 1000 µM was obtained with the resultant detection limit of 0.3 µM. Moreover, the AI-FIL based fluoroprobe was successfully utilized for Cr(VI) and AA detection in food and water samples with satisfactory accuracy and precision.

MetaBIDx: a new computational approach to bacteria identification in microbiomes.

Objectives: This study introduces MetaBIDx, a computational method designed to enhance species prediction in metagenomic environments. The method addresses the challenge of accurate species identification in complex microbiomes, which is due to the large number of generated reads and the ever-expanding number of bacterial genomes. Bacterial identification is essential for disease diagnosis and tracing outbreaks associated with microbial infections. Methods: MetaBIDx utilizes a modified Bloom filter for efficient indexing of reference genomes and incorporates a novel strategy for reducing false positives by clustering species based on their genomic coverages by identified reads. The approach was evaluated and compared with several well-established tools across various datasets. Precision, recall, and F1-score were used to quantify the accuracy of species prediction. Results: MetaBIDx demonstrated superior performance compared to other tools, especially in terms of precision and F1-score. The application of clustering based on approximate coverages significantly improved precision in species identification, effectively minimizing false positives. We further demonstrated that other methods can also benefit from our approach to removing false positives by clustering species based on approximate coverages. Conclusion: With a novel approach to reducing false positives and the effective use of a modified Bloom filter to index species, MetaBIDx represents an advancement in metagenomic analysis. The findings suggest that the proposed approach could also benefit other metagenomic tools, indicating its potential for broader application in the field. The study lays the groundwork for future improvements in computational efficiency and the expansion of microbial databases.

Designing optimal prototype filters for maximally decimated Cosine Modulated filter banks with rapid convergence.

An analytic design of a prototype filter for M-channel maximally decimated cosine-modulated Near Perfect Reconstruction (NPR) filter banks is proposed in this work. The prototype filter is created using the least-square (CLS) method with weighted constraints, which is one-dimensional and requires single-parameter optimization. Compared to existing approaches, this suggested method achieves rapid convergence by analytically determining the optimal step size, ensuring the 3 dB cutoff frequency at π/2 M. The simulation results for design examples outperform the techniques in the available literature in terms of amplitude and aliasing distortion, reaching distortion around 2.4489 × 10-4 and 3.4907 × 10-9, respectively. This optimization algorithm's usefulness is further demonstrated with the sub-band coding of ECG signals. Implementing optimal prototype filters has tangible real-world effects, especially in critical sectors like healthcare and communications, improving diagnostics accuracy, data transmission efficiency, and overall performance.

Comparing Mn-based oxides filters started by KMnO4 versus K2FeO4 for ammonium and manganese removal: Formation mechanism of active species.

A pilot-scale filtration system was adopted to prepare filter media with catalytic activity to remove manganese (Mn2+) and ammonium (NH4+-N). Three different combinations of oxidants (KMnO4 and K2FeO4) and reductants (MnSO4 and FeCl2) were used during the start-up period. Filter R3 started up by KMnO4 and FeCl2 (Mn7+→MnOx) exhibited excellent catalytic property, and the NH4+-N and Mn2+ removal efficiency reached over 80% on the 10th and 35th days, respectively. Filter R1 started up by K2FeO4 and MnSO4 (MnOx←Mn2+) exhibited the worst catalytic property. Filter R2 started up by KMnO4 and MnSO4 (Mn7+→MnOx←Mn2+) were in between. According to Zeta potential results, the Mn-based oxides (MnOx) formed by Mn7+→MnOx performed the highest pHIEP and pHPZC. The higher the pHIEP and pHPZC, the more unfavorable the cation adsorption. However, it was inconsistent with its excellent Mn2+ and NH4+-N removal abilities, implying that catalytic oxidation played a key role. Combined with XRD and XPS analysis, the results showed that the MnOx produced by the reduction of KMnO4 showed early formation of buserite crystals, high degree of amorphous, high content of Mn3+ and lattice oxygen with the higher activity to form defects. The above results showed that MnOx produced by the reduction of KMnO4 was more conducive to the formation of active species for catalytic oxidation of NH4+-N and Mn2+ removal. This study provides new insights on the formation mechanisms of the active MnOx that could catalytic oxidation of NH4+-N and Mn2+.

Remote path-following control for a holonomic Mecanum-wheeled robot in a resource-efficient networked control system.

This paper introduces a novel resource-efficient control structure for remote path-following control of autonomous vehicles based on a comprehensive combination of Kalman filtering, non-uniform dual-rate sampling, periodic event-triggered communication, and prediction-based and packet-based control techniques. An essential component of the control solution is a non-uniform dual-rate extended Kalman filter (NUDREKF), which includes an h-step ahead prediction stage. The prediction error of the NUDREKF is ensured to be exponentially mean-square bounded. The algorithmic implementation of the filter is straightforward and triggered by periodic event conditions. The main goal of the approach is to achieve efficient usage of resources in a wireless networked control system (WNCS), while maintaining satisfactory path-following behavior for the vehicle (a holonomic Mecanum-wheeled robot). The proposal is additionally capable of coping with typical drawbacks of WNCS such as time-varying delays, and packet dropouts and disorder. A Simscape Multibody simulation application reveals reductions of up to 93% in resource usage compared to a nominal time-triggered control solution. The simulation results are experimentally validated in the holonomic Mecanum-wheeled robotic platform.

An optimized filter design approach for enhancing imaging quality in industrial linear accelerator.

BACKGROUND: The polychromatic X-rays generated by a linear accelerator (Linac) often result in noticeable hardening artifacts in images, posing a significant challenge to accurate defect identification. To address this issue, a simple yet effective approach is to introduce filters at the radiation source outlet. However, current methods are often empirical, lacking scientifically sound metrics. OBJECTIVE: This study introduces an innovative filter design method that optimizes filter performance by balancing the impact of ray intensity and energy on image quality. MATERIALS AND METHODS: Firstly, different spectra under various materials and thicknesses of filters were obtained using GEometry ANd Tracking (Geant4) simulation. Subsequently, these spectra and their corresponding incident photon counts were used as input sources to generate different reconstructed images. By comprehensively comparing the intensity differences and noise in images of defective and non-defective regions, along with considering hardening indicators, the optimal filter was determined. RESULTS: The optimized filter was applied to a Linac-based X-ray computed tomography (CT) detection system designed for identifying defects in graphite materials within high-temperature gas-cooled reactor (HTR), with defect dimensions of 2 mm. After adding the filter, the hardening effect reduced by 22%, and the Defect Contrast Index (DCI) reached 3.226. CONCLUSION: The filter designed based on the parameters of Average Difference (AD) and Defect Contrast Index (DCI) can effectively improve the quality of defect images.

Comparative analysis of membrane filter diameters for detection of selected viruses in wastewater samples.

Wastewater serves as a valuable source of information as it contains biological markers that have been shed by infected individuals and from other biological organisms such as plants and animals. Wastewater has been proven to indicate the presence of emerging pathogens in a community before the manifestation of clinical symptoms. Several methods of concentration and nucleic acid extraction have been employed all around the world without a unified method. One such method involves the use of the adsorption extraction method (AE-method), which involves the use of electronegative membrane filters of different pore sizes. The membrane filters also differ by diameter, but no study has been reported on the effect of diameter on capture efficiency. This study was aimed at evaluating the comparative capture efficiency of two different membrane filter diameters of 45 and 90 mm with pore sizes of 0.45 µm for the detection of indicative and pathogenic viruses. Primary influent samples were obtained from two wastewater treatment plants in Baltimore, Maryland, between April 27 and June 29, 2023. A total of twenty samples were processed using 45- and 90-mm membrane filters. Nucleic acids were extracted from the filters using the QiAmp Viral RNA Mini Kit and assayed for four different targets: PMMoV, Norovirus (GI and GII), and CrAssphage by RT-qPCR. The result showed that 45 mm membrane filters had a higher combined mean capture efficiency in log10 gene copies per liter (gc/l) for crAssphage (7.40) than 90 mm membrane filters (7.10). Similarly, the 45-mm filter had higher mean capture efficiency for norovirus GI (4.67) than the 90-mm filter (1.84) and likewise for Norovirus GII (2.14, 1.04). On the contrary, 90-mm membrane filters were observed to have better capture of PMMoV (6.84) compared to 45-mm membrane filters (6.69). This result therefore implies that 45-mm membrane filters could be more efficient for wastewater surveillance studies through the AE method for indicator viruses like CrAssphage and human disease-causing viruses like Norovirus.

Leaf: an ultrafast filter for population-scale long-read SV detection.

Advances in sequencing technology have facilitated population-scale long-read structural variant (SV) detection. Arguably, one of the main challenges in population-scale analysis is developing effective computational pipelines. Here, we present a new filter-based pipeline for population-scale long-read SV detection. It better captures SV signals at an early stage than conventional assembly-based or alignment-based pipelines. Assessments in this work suggest that the filter-based pipeline helps better resolve intra-read rearrangements. Moreover, it is also more computationally efficient than conventional pipelines and thus may facilitate population-scale long-read applications.

A novel coupling process to replace the traditional multi-stage anammox process-sulfur autotrophic denitrification coupled anammox system.

A novel coupling process to replace the traditional multi-stage anammox process-sulfur autotrophic denitrification (SAD) coupled anaerobic ammonium oxidation (anammox) system was designed, which solved problems of nitrate produced in anammox process and low nitrate conversion rate caused by nitrite accumulation in SAD process. Different filter structures (SAD filter and anammox granular sludge) were investigated to further explore the excellent performance of the novel integrated reactor. The results of sequential batch experiments indicated that nitrite accumulation occurred during SAD, which inhibited the conversion of nitrate to dinitrogen gas. When SAD filter and anammox granular sludge were added to packed bed reactor simultaneously, the nitrate removal rate increased by 37.21% and effluent nitrite concentration decreased by 100% compared to that achieved using SAD. The stratified filter structure solved groove flow. Different proportion influence of SAD filter and anammox granular sludge on the stratified filter structure was evaluated. More suitable ratio of SAD filter to anammox granular sludge was 2:1. Proteobacteria (57.26%), Bacteroidetes (20.12%) and Chloroflexi (9.95%) were the main phyla. The dominant genera of denitrification functional bacteria were Thiobacillus (39.80%), Chlorobaculum (3.99%), norank_f_PHOs-HE36 (2.90%) and Ignavibacterium (2.64%). The dominant genus of anammox bacterium was Candidatus_Kuenenia (3.05%).

Improved performance of a shunt hybrid active power filter by a robust exponential functional link network-based nonlinear adaptive filter control to enhance power quality.

This research article details the design and implementation of a nonlinear adaptive filtering (NAF) technique using an exponential functional link network (EFLN) for a shunt hybrid active power filter (SHAPF) control to solve the current-associated power quality issues on the utility side at the distribution level of electrical power systems. Separation of the fundamental component from the harmonics, achieving unity power factor operation, reducing the reactive power drawn from the source, balancing the currents during transients, and reduction of total harmonic distortion (THD) of the source current are the issues considered to resolve. The proposed technique solves these issues by generating the sinusoidal reference current and separating the fundamental current from the harmonics. When compared to conventional and existing adaptive filtering techniques such as least mean square (LMS), least mean fourth (LMF), and variable step size LMS (VSS-LMS), the proposed EFLN-NAF method excels in terms of speedy convergence, adaptability in noise-specific environments and reduced steady-state coefficient error. MATLAB/Simulink software is utilized to perform the simulations to examine the suggested strategy for the chosen SHAPF topology both in static and dynamic scenarios. For a 15 kW and 3kVAr requirement of the nonlinear load, simulation results proved that the designed PPF for 2kVAr is able to share the reactive power with the APF, thereby reducing its rating and cost. The proposed method of filtering has been proven to be fast converging with 0.049 s, and the THD in steady state is brought to 1.32 % in steady-state and to 3.77 % during transient conditions, which are under standard limits. A hardware prototype of the experimental setup is constructed at the laboratory scale with OPAL-RT (OP4510) as the controller. With an active and reactive power demand of 1.1 kW and 210VAr, the designed PPF supplies 110 VAr, whereas the rest is supplied by the APF. The practical THD in source current is observed to be 2.081 %, which meets the standards. The results from both simulations and experiments are validated, and the efficacy of the proposed technique in mitigating the aforementioned power quality issues is proved.

Asparagus officinalis Herb-derived Carbon Quantum Dots: Luminescent Probe for Medical Diagnostics.

The utilization of natural materials for the synthesis of highly fluorescent carbon quantum dots (CQDs) presents a sustainable approach to overcome the challenges associated with traditional chemical precursors. Here, we report the synthesis of novel S,N-self-doped CQDs (S,N@CQDs) derived from asparagus officinalis herb. These S,N@CQDs exhibit 16.7% fluorescence quantum yield, demonstrating their potential in medical diagnostics. We demonstrate the efficacy of S,N@CQDs as luminescent probes for the detection of anti-pathogenic medications metronidazole (MTZ) and nitazoxanide (NTZ) over concentration ranges of 0.0-180.0 µM (with a limit of detection (LOD) of 0.064 µM) and 0.25-40.0 µM (LOD of 0.05 µM), respectively. The probes were successfully applied to determine MTZ and NTZ in medicinal samples, real samples, and spiked human plasma, with excellent recovery rates ranging from 99.82% to 103.03%. Additionally, S,N@CQDs demonstrate exceptional efficacy as diagnostic luminescent probes for hemoglobin (Hb) detection over a concentration range of 0-900 nM, with a minimal detectability of 9.24 nM, comparable to commercially available medical laboratory diagnostic tests. The eco-friendly synthesis and precise detection limits of S,N@CQDs meet necessary analytical requirements and hold promise for advancing diagnostic capabilities in clinical settings. This research signifies a significant step towards sustainable and efficient fluorescence-based medical diagnostics.