Particle Filter Research Articles

Multi-Sensor fusion and semantic map-based particle filtering for robust indoor localization

To enhance positioning accuracy and ensure long-term stability in large-scale indoor environments, this paper presents a robust particle filtering method that integrates IMU, magnetic field, Bluetooth, and semantic map data, implemented through a handheld portable device. In the particle prediction phase, based on peak candidates extracted in the time domain, the unintentional interference movement of the hand is removed through short-time Fourier transform in the frequency domain to reduce the noise of pedestrian dead reckoning. During the observation phase, we introduce a magnetic interference coefficient to model and quantify the degree of electromagnetic interference in different environments, thereby improving the accuracy of indoor magnetic headings and enhancing the alignment of particle swarms with real-world directional constraints. Furthermore, based on passable semantic information, the indoor map is segmented into distinct probability regions, allowing the particle filter to adaptively adjust the weight of the particle swarm when moving between regions, thus improving the robustness of the overall fusion system by preventing particles from entering infeasible areas. Experiments and validations were carried out in a large shopping mall and parking lot with multiple devices. Results demonstrate that the proposed method improves the average positioning accuracy by approximately 25% compared to the Bluetooth-only positioning system, with a 15% increase in cumulative distribution probability within 3 m.

Investigation of diesel particulate filter performance under typical failure conditions

Diesel particulate filter (DPF) is one of the most effective particulate reduction components in diesel engine aftertreatment systems. However, prolonged utilization of a DPF might result in a deterioration in performance or even failure, such as ash-clogging, melting, breakage, and catalyst deactivation. It is crucial to conduct research on DPF performance degradation across a series of partially failed conditions. In this study, the morphology and chemical composition of the ash in the DPF samples were characterized. The effects of degree-varying clogging and unplugged-breakage failure on exhaust characteristics as well as DPF temperature, filtration and pressure drop characteristics were investigated. Finally, the sensitivity differences between DPF performance parameters and failure index parameters were examined using a canonical correlation analysis (CCA). When four factors were considered, the ash loading index and breakage index had the greatest impact on DPF temperature difference and filtration efficiency, while the exhaust mass flow rate had the greatest impact on pressure drop. Besides, the influence of the DPF inlet temperature on the pressure drop, filtration efficiency, and temperature difference was not significant. These findings contribute to the prediction of DPF performance in partially failed conditions and the development of DPF failure diagnosis methods.

Predictive modeling using Copula Particle Filter and Adaptive Network-Based Fuzzy Inference

This paper introduces a novel prediction algorithm, CPF-ANFIS, designed to overcome the challenges posed by high-dimensional input data in Adaptive Neuro-Fuzzy Inference Systems (ANFIS). ANFIS's performance deteriorates with increasing input dimensionality due to the distortion of its membership functions. To address this limitation, CPF-ANFIS leverages a two-stage approach: A Copula Particle Filter (CPF) for robust state estimation and ANFIS for nonlinear mapping. By incorporating copulas, CPF effectively addresses the impoverishment and degeneracy problems commonly encountered in traditional particle filters. This enhanced robustness allows for more accurate state estimation, which in turn improves the overall performance of the CPF-ANFIS algorithm. By decoupling state estimation from nonlinear modeling, CPF-ANFIS effectively mitigates the curse of dimensionality. The proposed method is evaluated on real-world applications, such as hybrid PV-wind systems and SLAM. Experimental results demonstrate that CPF-ANFIS consistently outperforms ANFIS and the Copula Particle Filter individually, as well as previously proposed methods such as ANFIS-PF, highlighting its effectiveness in achieving accurate predictions under challenging conditions. The results show that the CPF-ANFIS algorithm increases prediction accuracy by at least 5% compared to using each algorithm separately.

Harnessing 12-lead ECG and MRI data to personalise repolarisation profiles in cardiac digital twin models for enhanced virtual drug testing

Cardiac digital twins are computational tools capturing key functional and anatomical characteristics of patient hearts for investigating disease phenotypes and predicting responses to therapy. When paired with large-scale computational resources and large clinical datasets, digital twin technology can enable virtual clinical trials on virtual cohorts to fast-track therapy development. Here, we present an open-source automated pipeline for personalising ventricular electrophysiological function based on routinely acquired magnetic resonance imaging (MRI) data and the standard 12-lead electrocardiogram (ECG).Using MRI-based anatomical models, a sequential Monte-Carlo approximate Bayesian computational inference method is extended to infer electrical activation and repolarisation characteristics from the ECG. Fast simulations are conducted with a reaction-Eikonal model, including the Purkinje network and biophysically-detailed subcellular ionic current dynamics for repolarisation. For each patient, parameter uncertainty is represented by inferring an envelope of plausible ventricular models rather than a single one, which means that parameter uncertainty can be propagated to therapy evaluation. Furthermore, we have developed techniques for translating from reaction-Eikonal to monodomain simulations, which allows more realistic simulations of cardiac electrophysiology. The pipeline is demonstrated in three healthy subjects, where our inferred pseudo-diffusion reaction-Eikonal models reproduced the patient's ECG with a median Pearson's correlation coefficient of 0.9, and then translated to monodomain simulations with a median correlation coefficient of 0.84 across all subjects. We then demonstrate our digital twins for virtual evaluation of Dofetilide with uncertainty quantification. These evaluations using our cardiac digital twins reproduced dose-dependent QTc and T peak to T end prolongations that are in keeping with large population drug response data.The methodologies for cardiac digital twinning presented here are a step towards personalised virtual therapy testing and can be scaled to generate virtual populations for clinical trials to fast-track therapy evaluation. The tools developed for this paper are open-source, documented, and made publicly available.

A radioactive source-seeking method based on angle constraint and particle diffusion

The deployment of mobile robots to conduct search for radioactive sources plays a crucial role in preventing radiation pollution and safeguarding public health. And it is a significant challenge to accurately locate radioactive sources in unknown environments. Herein, a particle filter based on mobile robot search method is proposed to localize radiation sources. By applying an angle constraint technique, the complexity of this algorithm is reduced, the high accuracy of source estimation is maintained, and the range of search is expanded. Additionally, a particle diffusion technology is applied to address particle loss that occurs during filtering. Furthermore, an adaptive gradient descent strategy is adopted to enhance the search efficiency. Experimental results demonstrate that this method achieves a success rate of over 95 % within a rectangular area (e.g., 40m by 40m), outperforming traditional methods, and it could perform effectively in dual radioactive source search tasks.

Research on Mixed Reality Hand Interaction Technology in Flight Simulation Teaching

Abstract The integration of mixed reality simulation training in institutional education and teaching processes can yield significant quality benefits. Non-wearable interactive methods represent a more natural form of hand interaction within flight cockpits, covering various emerging fields such as mixed reality, computer vision, and human-computer interaction. This paper is guided by a “human-centered” approach to natural human-computer interaction and proposes a virtual hand mixed reality interaction scheme based on hand natural feature points. This study introduces a hand detection, segmentation, and recognition algorithm based on skin colour and key hand feature points. Initially, the algorithm uses the Otsu adaptive threshold algorithm in the YCbCr space for skin colour detection to accommodate varying image brightness levels. Subsequently, it employs a hand Keypoint model for rapid hand detection within skin-like areas while excluding interference from other regions. During the process of hand tracking, optimization prediction is performed using the particle filter algorithm combined with the artificial fish swarm algorithm to achieve tracking accuracy within six pixels in both horizontal and vertical directions. Gesture recognition involves extracting Fourier descriptive contour features of the hand bone structure through identification of feature points. Furthermore, this study combines the optimization capabilities of the artificial fish swarm algorithm to enhance support vector machine model parameter optimization for improved recognition rates. Testing involved 800 processed images as part of a test set for gesture recognition; only five commonly used gestures within flight cockpits were recognized and classified with an accuracy rate reaching up to 98%. Finally, this research presents virtual representations of common operational gestures including natural hand states, control stick manipulation, and throttle handling positions, button activations, and rotational movements via head-mounted displays or screens. The proposed mixed reality-based hand gesture interaction system addresses challenges related to complex background interference during hand detection in cockpit environments as well as issues arising from lighting disturbances. Additionally it resolves problems associated with insufficient particles leading to tracking failures while significantly enhancing registration effects during tracking processes. Moreover it mitigates issues related to Fourier descriptive features being influenced by background variations or changes in pose along with limitations pertaining to expressing singular forms of gestures thereby improving overall classification accuracy.

Magneto-inductive positioning network based on magnetic energy density

The magneto-inductive positioning system (MPS) is considered a promising solution for realizing accurate positioning in challenging environments, particularly to support the development of emerging technologies. However, obtaining the position of magnetic beacons increases the system’s complexity and the sensor’s attitude error lowers the positioning performance. To solve the above problems, this paper proposes an improved magneto-inductive positioning network system (MPNS) based on magnetic energy density (MED). An orientation model without the sensor’s attitude is derived from MED. An incrementally deployed magneto-inductive positioning network is developed with the orientation model, which reduces the system complexity. An advanced resampling method of particle filter is employed in the MPNS to improve the positioning accuracy. The realized system displays the max error of no more than 0.13 m for the static target . The performance of MPNS that tracks moving targets is examined with simulation. The results exhibit that 99% of tracking errors are less than 0.25 m.

Assimilation of Sentinel‐Based Leaf Area Index for Modeling Surface‐Ground Water Interactions in Irrigation Districts

AbstractVegetation‐related processes, such as evapotranspiration (ET), irrigation water withdrawal, and groundwater recharge, are influencing surface water (SW)—groundwater (GW) interaction in irrigation districts. Meanwhile, conventional numerical models of SW‐GW interaction are not developed based on satellite‐based observations of vegetation indices. In this paper, we propose a novel methodology for multivariate assimilation of Sentinel‐based leaf area index (LAI) as well as in‐situ records of streamflow. Moreover, the GW model is initially calibrated based on water table observations. These observations are assimilated into the SWAT‐MODFLOW model to accurately analyze the advantage of considering high‐resolution LAI data for SW‐GW modeling. We develop a data assimilation (DA) framework for SWAT‐MODFLOW model using the particle filter based on the sampling importance resampling (PF‐SIR). Parameters of MODFLOW are calibrated using the parameter estimation (PEST) algorithm and based on in‐situ observation of the GW table. The methodology is implemented over the Mahabad Irrigation Plain, located in the Urmia Lake Basin in Iran. Some DA scenarios are closely examined, including univariate LAI assimilation (L‐DA), univariate streamflow assimilation (S‐DA), and multivariate streamflow‐LAI assimilation (SL‐DA). Results show that the SL‐DA scenario results in the best estimations of streamflow, LAI, and GW level, compared to other DA scenarios. The streamflow DA does not improve the accuracy of LAI estimation, while the LAI assimilation scenario results in significant improvements in streamflow simulation, where, compared to the open loop run, the (absolute) bias decreases from 75% to 6%. Moreover, S‐DA, compared to L‐DA, underestimates irrigation water use and demand as well as potential and actual crop yield.

A novel constrained skew-Gaussian filter and its application to maneuverable reentry vehicle tracking

The state of the system generally satisfies specific constraints imposed by material properties or physical laws, so the application of these constraints can improve the accuracy of state estimation. In this paper, a novel recursive filter referred as constrained high-degree cubature skew-Gaussian filter (CHCSGF) is proposed, which achieves soft-constrained state estimation by compressing the probability density of unconstrained states with constraint information. First, the probability density of the state under inequality soft constraints is modeled as a skew-Gaussian (SG) distribution, rather than truncated or single Gaussian distributions. Then, a recursive constrained SG filter is developed to handle inequality soft constraints in linear systems. Addressing nonlinear challenges, a 5th-degree spherical-radial cubature approximation method is presented to numerically calculate SG-weighted integrals for the nonlinear transformation of SG distribution. Finally, the CHCSGF algorithm is proposed using this method to tackle nonlinear filtering problems. The CHCSGF is applied to reentry trajectory tracking to improve estimation accuracy by dealing with heat flow, dynamic pressure and overload constraints during reentry flight. Simulation results demonstrate that the CHCSGF achieves higher estimation accuracy than unconstrained methods under nonlinear inequality soft constraints, and is robust to the constraints with a prior error. Compared to particle filter and moving horizon estimation, the computational complexity of CHCSGF is significantly reduced.

Aerosol deposition on face masks in an open environment during inhalation

The aerodynamics of aerosols and their deposition on face masks play a critical role in determining the effectiveness of respiratory protection. While existing studies have focused on the risks associated with aerosol dispersion during exhalation, little attention has been paid to aerosol aerodynamics in an open environment, where aerosols can circumvent masks, during inhalation. This is because mask performance has primarily been evaluated by the particle filtration efficiency in closed pipe setups, which do not account for the aerodynamics of aerosols around the wearer's face. In this study, we conduct experiments in an open environment to investigate the aerosol flow around a face mask and the aerosol deposition under varying inhalation pressures. Our results indicate that an aerosol flow near a mask surface behaves like a viscous flow, stagnating within the range of human inhalation. Within this range, we find that the amount of aerosol deposited can be predicted by modifying existing aerodynamics theory. Using a theoretical model, a critical inhalation pressure is identified at which water aerosols begin to penetrate through a mask. Finally, we propose the aerosol circumvention efficiency as a new metric to assess mask performance in open environments by taking into account the effects of aerosol circumvention.

A sound-vibration physical-information fusion constraint-guided deep learning method for rolling bearing fault diagnosis

Although current deep learning models for bearing fault diagnosis have achieved excellent accuracy, the lack of constraint-guided learning of the physical mechanisms of real bearing failures and a physically scientific training paradigm leads to low interpretability and unreliability of intelligent fault diagnosis models. In this study, a sound-vibration physical-information fusion constraint-guided (PFCG) deep learning (DL) method is proposed, aiming at weighted fusion of sound and vibration multi-physical information into a deep learning model, to guide the DL model to learn more realistic physical laws of bearing failure. Firstly, a 15-degree-of-freedom nonlinear dynamics model of multi-stage degraded bearing failure mechanism with sound-vibration response is developed, which considers the evolutionary mechanism of bearing failure from healthy state to different stages, and utilizes a particle filtering algorithm for dynamic calibration of hidden parameters. Moreover, a lightweight DL fault diagnosis model is designed to realize the deep interaction between the physical model and the DL model through the weighted fusion of the cross-entropy loss function, physical consistency loss and uncertainty loss. Moreover, the superior diagnostic performance of the proposed sound and vibration PFCG-DL model is verified by comparing the performance fluctuations and parameter attributes of different DL benchmark models before and after being guided by physical information fusion constraints (PFCG). Eventually, the proposed PFCG-Transformer model achieves a diagnostic accuracy of 99.45% while keeping the number of parameters at only 0.62M, which significantly improves the accuracy and reduces the computational complexity by 81.5% compared to the CAME-Transformer model's 3.24 M number of parameters and 95.00% diagnostic accuracy. In addition, the test time of PFCG-Transformer is reduced to 1.02 s, which is 60.2% less than CAME-Transformer, demonstrating higher computational efficiency and real-time performance. Importantly, in terms of interpretability, the engineering interpretability and credibility of the models are further improved by visualizing the feature learning results of the vibration modal and multimodal fusion models and the sensitivity analyses of the sound-vibration response models with internal and external physical hyperparameters. Therefore, this study proposes a physical information-guided deep learning method with strong interpretability and superior performance, which provides an important reference for further research and application in the field of bearing fault diagnosis.

Modifying Sequential Monte Carlo Optimisation for Index Tracking to Allow for Transaction Costs

Managing a portfolio whose value closely tracks an index by trading only in a subset of the index constituents involves an NP-hard optimisation problem. In the prior literature, it has been suggested that this problem be solved using sequential Monte Carlo (SMC, also known as particle filter) methods. However, this literature does not take transaction costs into account, although transaction costs are the primary motivation for attempting to replicate the index by trading in a subset, rather than the full set of index constituents. This paper modifies the SMC approach to index tracking to allow for proportional transaction costs and implements this extended method on empirical data for a variety stock indices. In addition to providing a more practically useful tracking strategy by allowing for transaction costs, we find that including a penalty for transaction costs in the optimisation objective can actually lead to better tracking performance.

Mobile laboratory measurements of air pollutants in Baltimore, MD elucidate issues of environmental justice

ABSTRACT The City of Baltimore, MD has a history of problems with environmental justice (EJ), air pollution, and the urban heat island (UHI) effect. Current chemical transport models lack the resolution to simulate concentrations on the scale needed, about 100 m, to identify the neighborhoods with anomalously high air pollution levels. In this paper we introduce the capabilities of a mobile laboratory and an initial survey of several pollutants in Baltimore to identify which communities are exposed to disproportionate concentrations of air pollution and to which species. High concentrations of black carbon (BC) stood out at some locations – near major highways, downtown, and in the Curtis Bay neighborhood of Baltimore. Results from the mobile lab are confirmed with longer-term, low-cost monitoring. In Curtis Bay, higher concentrations of BC were measured along Pennington Ave. (mean [5th to 95th percentiles] = 2.08 [2.0–10.9] μg m−3) than along Curtis Ave. just ~ 150 m away (0.67[0.1 – 1.8] μg m−3). Other species, including criteria pollutants ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and fine particulate matter (PM2.5), showed little gradient. Observations with high spatial and temporal resolution help isolate the mechanisms leading to locally high pollutant concentrations. The difference in BC appears to result not from heavier truck traffic or slower dispersion but from the interruptions in traffic flow. Pennington Ave. has three stoplights while Curtis Ave. has none. As heavy-duty diesel-powered vehicles accelerate, they experience turbo-lag and the resulting rich air-fuel mixture exacerbates BC emissions. Immediate mediation might be achieved through smoother traffic flow, and the long-term solution through replacing heavy-duty trucks with electric vehicles. Implications: We present results documenting the locations within Baltimore of high concentrations of Black Carbon pollution and identify the likely source – diesel exhaust emissions exacerbated by stop-and-go traffic and associated turbo-lag. This suggests solutions (smoother traffic, retrofit particulate filters, replacement of diesel with electric vehicles) that would enhance Environmental Justice (EJ) and could be applied to other cities with EJ problems. Synopsis: This paper presents observations of atmospheric black carbon aerosol showing impacts on environmental justice, then identifies causes and suggests solutions.

Multifunctional air filter with enhanced filtration, formaldehyde degradation, and antibacterial properties using PET, PDA, and RGO-TiO2

Currently, most air filters, such as polyester (PET) fiber non-woven fabric, only have a single function of particulate filtration. However, the removal of substances harmful to human health, such as volatile organic compounds (VOCs) and bacteria, is becoming increasingly important in indoor air purification. Herein, based on the biomimetic surface functionalization properties of polydopamine (PDA) and the photocatalytic degradation of organic matter and the antibacterial effect of RGO (reduced graphene oxide)-TiO2, a multifunctional NWP2/PET-PDA-RGO-TiO2(P/PPRT) filter with a sandwich structure was prepared for formaldehyde degradation, aerosol filtration, and bactericidal operation. The P/PP2R0.05T20 maintains optimal performance of photocatalytic reaction kinetics and reaches a formaldehyde degradation rate of higher than 79 % within 300 min in the 7th cycle of usage. Compared with the base subtracts, P/PP2R0.05T20 has the highest filtration efficiency of 88.57–99.18 % for 0.3–10 µm particles, respectively, and has a filtration efficiency of higher than 74 % for 0.3 µm particles in the 7th cycles of usage. The quality factor of P/PP2R0.05T20 is improved significantly due to the sandwich structure. The effect of loaded particulate on the catalyst for photocatalytic degradation of formaldehyde was also studied. It was found that the photocatalytic formaldehyde degradation rate of P/PP2R0.05T20 is about 81.8 % within 300 min under different A2 particulate load content. In addition, P/PP2R0.05T20 has a bioaerosol removal rate of higher than 98 % against E. coli. This study provides a practical approach to preparing a multifunctional air filter with the performance of aerosol filtration, photocatalytic degradation of formaldehyde, and bactericidal function using PET non-woven fabric.

Using posture recognition algorithms based on machine learning to identify senior health

Faced with the situation that the elderly people at home have dangerous behaviors, the study explores various aspects of motion target detection, real-time target tracking and behavioral pose recognition and classification, using behavioral poses in videos as samples. To tackle the challenges in detecting motion targets, a target detection method based on Gaussian mixture model (GMM) and four frame difference method is proposed; A tracking technique incorporating Kalman filter (KF) is investigated to trail the behavioral changes of the elderly in actual time. A seven-layer convolutional neural network (CNN) is constructed to face the problem of inaccurate behavioral pose recognition. Through relevant experimental analyses, the outcomes show that the increased GMM detection way has a complete profile and the accuracy is significantly improved. The KF target tracking technique can trail the object trajectory in actual time and steadily, with the smallest trailing error value of 0.19. The classification accuracy of the CNN pose recognition model is 95.87%, and the pose classification time is 27 seconds. Its performance is superior to the mean shift algorithm, particle filter algorithm, and Cam Shift algorithm in all aspects. When applied in practice, it can accurately identify whether the elderly’s behavior is abnormal and ensure their daily health.

Detecting rough volatility: a filtering approach

In this paper, we focus on filtering and parameter estimation in stochastic volatility models when observations arise from high-frequency data. We are particularly interested in rough volatility models where spot volatility is driven by fractional Brownian motion with Hurst index H < 1 2 . Since volatility is not directly observable, we rely on particle filtering techniques for statistical inference regarding the current level of volatility and the parameters governing its dynamics. In order to obtain numerically efficient and recursive algorithms, we use the fact that a fractional Brownian motion can be represented through a superposition of Markovian semimartingales (Ornstein-Uhlenbeck processes). We analyze the performance of our approach on simulated data and we compare it to similar studies in the literature. The paper concludes with an empirical case study, where we apply our methodology to high-frequency data of a liquid stock.

Experimental and numerical analysis of particulate matter deposition in DPF for different blends of Algae Bio diesel

The stringent emission norms have compelled to use Diesel particulate filter (DPF) to reduce particulates emission in automotive Diesel engine. The back pressure developed in the DPF due to the Particulates matter (PM) deposition in porous zone degrades the diesel engine’s performance. So, there is a need of a fuel which should produce less particulates on combustion as well as its produced particulates should be easy to get regenerated inside the DPF. Three different Algae biodiesel blends B20, B40 and B60 were selected for the study, while the results are compared with the diesel fuel. A numerical study has been done with diesel and Algae biodiesel blends to investigate the effect of PM deposition on the DPF performance. The results of PM concentration and the pressure drop has been predicted for t = 2000s. and compared with the results predicted for diesel fuel. The summarized velocity contours and the PM concentration plots show that a maximum of the PM concentration was found in the diesel fuel case, while the lower found in B60 algae blend. Also, the pressure drop was found to increase with the increase in PM deposition in each case of fuel. The SEM-EDS analysis is done after collecting PM samples after combustion shows a higher percentage of Oxygen and lower Carbon with an increment of Biodiesel in the blend. This work provides a brief idea about the PM deposition in DPF while using Diesel and Algae Biodiesel blends and highlighting the better fuel option for Diesel Engine.

An improved sequential importance sampling method for structural reliability analysis of high dimensional problems

Reliability analysis for rare events has proven to be a significant challenge, particularly in non-linear and complex scenarios. Despite the effectiveness of Sequential Importance Sampling (SIS) and Subset Simulation (SUS) in addressing high-dimensional small failure probability problems, the computational burden associated with mechanical simulations for complex structures remains substantial. To address this issue, this paper introduces a novel approach referred to as SIS and Kriging Metamodel Integration (SISAK), designed for computing small failure probabilities in high dimensions. This method involves generating a small number of samples from the original distribution to construct an adaptive metamodel, which is then iteratively optimized using a series of intermediate distributions introduced by SIS. By substituting the function for intermediate distributions with a continuously optimized metamodel, the enhanced approach significantly reduces the computational load associated with mechanical model evaluations compared to the direct use of SIS. This approach does not depend on determining the most probable failure point and does not require consideration of the failure domain shape. Additionally, it inherits the advantages of SIS in addressing high-dimensional small failure probabilities, making it well-suited for structural reliability analysis of nonlinear systems, discontinuous failure domains, cascading functions, and high-dimensional problems.

3D Porous Nanocellulose Based Filter from Palm Bunch Using Tert‐Butyl Alcohol‐Assisted Pore Inducive Technique for Airborne Particulate Matter Retention

AbstractEnvironmental hazards, especially particulates, and microbiological pollutants, have resulted in significant negative impacts on human health. In this study, 3D biodegradable cellulose filters were made from nanocellulose and tested for the removal efficiency of airborne particulates. Cellulose was first extracted from palm empty fruit bunches (EFBs) using green Deep Eutectic Solvents (DESs) under moderate temperature and then homogenized at high pressure to produce cellulose at the nanoscale size. Three types of renewable choline chloride (ChCl)‐based DESs were used: lactic acid, 1,3‐butanediol, and oxalic acid. The maximum cellulose yield from DES pretreatment was 38.78 % based on raw EFB (100 % cellulose yield based on cellulose in EFB) with ChBu60 C and the maximum nanocellulose yield was 68.49 % based on cellulose in EFB with ChLa80 C after 12‐pass high pressure homogenization. The cellulose air filter was fabricated using tert‐butyl alcohol (tBuOH) solvent exchanged under freeze‐drying conditions and characterized by different state‐of‐the‐art techniques. It was shown that the ChBu80 C filter had the lowest pressure drop (10.16 mmH2O or 2.07 mmH2O cm−2) and the maximum particle filtration efficiency (32.51 % for 0.1 μm and 93.63 % for 1.0 μm particles). The process simulation and techno‐economic analysis were performed for nanocellulose production and air filter fabrication to select the most feasible technology.

Methodological assessment for efficient collection of Schistosoma mansoni environmental DNA and improved schistosomiasis surveillance in tropical wetlands

Schistosomiasis, caused by trematodes of genus Schistosoma, is among the most seriously neglected tropical diseases. Although rapid surveillance of risk areas for Schistosoma transmission is vital to control schistosomiasis, the habitat and infection status of this parasite are difficult to assess. Environmental DNA (eDNA) analysis, involving the detection of extra-organismal DNA in water samples, facilitates cost-efficient and sensitive biomonitoring of aquatic environments and is a promising tool to identify Schistosoma habitat and infection risk areas. However, in tropical wetlands, highly turbid water causes filter clogging, thereby decreasing the filtration volume and increasing the risk of false negatives. Therefore, in this study, we aimed to conduct laboratory experiments and field surveys in Lake Victoria, Mbita, to determine the appropriate filter pore size for S. mansoni eDNA collection in terms of particle size and filtration volume. In the laboratory experiment, aquarium water was sequentially filtered using different pore size filters. Targeting >3 µm size fraction was found to be sufficient to capture S. mansoni eDNA particles, regardless of their life cycle stage (egg, miracidia, and cercaria). In the field surveys, GF/D (2.7 µm nominal pore size) filter yielded 2.5-times the filtration volume obtained with a smaller pore size filter and pre-filtration methods under the same time constraints. Moreover, a site-occupancy model was applied to the field detection results to estimate S. mansoni eDNA occurrence and detection probabilities and assess the number of water samples and PCR replicates necessary for efficient eDNA detection. Overall, this study reveals an effective method for S. mansoni eDNA detection in turbid water, facilitating the rapid and sensitive monitoring of its distribution and cost-effective identification of schistosomiasis transmission risk areas.