Application Of Filter Research Articles

Establishment of predictive models for head loss and filtration efficiency of pontoon mesh rotary filter based on dimension analysis

Filters serve as core equipment to ensure the normal operation of micro-irrigation systems, with head loss and filtration efficiency serving as the two key indicators for evaluating performance. In this study, we used a pre-pump filter-pontoon mesh rotary filter as the research object and conducted physical model tests under the flow rate (798–1050 L1 h−1), sand content (0.5–2.5 g1 L−1), and aperture of the filter screen (0.125–0.180 mm). We then adopted range analysis, variance analysis (ANOVA), dimensional analysis, and the multiple linear regression (MLR) method to analyze the results. The results showed that the order of factors affecting the head loss of the assessment indices, from large to small, was as follows: flow rate, sand content, and aperture of the filter screen. The order of factors affecting the filtration efficiency of the assessment indices from large to small was as follows: sand content, flow rate, and aperture of the filter screen. Predictive models for head loss and filtration efficiency were developed, with coefficients of determination R2 of 0.969 and 0.954, and root mean square error (RMSE) values of 0.1041 and 0.0183. The model exhibited high accuracy and could be used to predict the head loss and filtration efficiency of the pontoon mesh rotary filter. In the test range, the optimal working condition of this filter was a flow rate of 930 L1 h−1, sand content of 2.0 g1 L−1, and 0.150 mm aperture of the filter screen. In addition, the head loss under this condition was 0.281 m, and the filtration efficiency was 84.01%. These results could serve as a reference for the further optimization and application of the pontoon mesh rotary filter, while also enriching the hydraulic performance and filtration performance of the pre-pump filter.

Application of Convolutional Neural Networks and Rolling Guidance Filter in Image Fusion for Detecting Brain Tumors

Medical image fusion is the technique of integrating images from several medical imaging modalities without causing any distortion or information loss. By preserving every feature in the fused image, it increases the value of medical imaging for the diagnosis and treatment of medical conditions. A novel fusion mechanism for multimodal image data sets is proposed in this paper. Each of the source image is smoothened using cross guided filter in the initial step. Guided filter output is further smoothened to remove fine structures using rolling guidance filter. Then details (high frequency) of each source image are extracted by subtracting the rolling guidance filter output from corresponding source image. These details are fed to convolutional neural networks to obtain decision maps. Finally the source images are fused based on decision map using maximum rule of combination. We assessed the performance of our suggested methodology using several pairs of medical imaging datasets that are accessible to the general public. According to the quantitative evaluation, the recommended fusion strategy for multimodal image fusion improves the average IE by 12.4%, MI by 41.8%, SF by 21.4%, SD by 22.81%, MSSIM by 31.1%, and by 39% when compared to existing methods, which makes it appropriate for use in the medical field for accurate diagnosis.

Visual Localization Domain for Accurate V-SLAM from Stereo Cameras

Trajectory estimation from stereo image sequences remains a fundamental challenge in Visual Simultaneous Localization and Mapping (V-SLAM). To address this, we propose a novel approach that focuses on the identification and matching of keypoints within a transformed domain that emphasizes visually significant features. Specifically, we propose to perform V-SLAM in a VIsual Localization Domain (VILD), i.e., a domain where visually relevant feature are suitably represented for analysis and tracking. This transformed domain adheres to information-theoretic principles, enabling a maximum likelihood estimation of rotation, translation, and scaling parameters by minimizing the distance between the coefficients of the observed image and those of a reference template. The transformed coefficients are obtained from the output of specialized Circular Harmonic Function (CHF) filters of varying orders. Leveraging this property, we employ a first-order approximation of the image-series representation, directly computing the first-order coefficients through the application of first-order CHF filters. The proposed VILD provides a theoretically grounded and visually relevant representation of the image. We utilize VILD for point matching and tracking across the stereo video sequence. The experimental results on real-world video datasets demonstrate that integrating visually-driven filtering significantly improves trajectory estimation accuracy compared to traditional tracking performed in the spatial domain.

Identification of Novel Progesterone Receptor (PR) Inhibitors (Homo sapiens) from Metabolites of Biotransformation Fungal: A Bioinformatics Approach

Background/Objectives: Steroids have demonstrated selective cytotoxic properties against tumor cells. The pro-gesterone receptor (PR) plays a vital role in the proliferation, cell differentiation, and maintenance of female reproductive tissue, and its malfunction can lead to breast cancer. The use of the biocatalytic method by filamentous fungi has sparked interest in the obtained of steroids due to the advantages of the process. Methods: Pharmacokinetic and toxicological properties (rat and mouse), molecular docking simulation studies, and prediction of the spectrum of biological activity were performed to select molecules with the potential for PR inhibition, from 155 biotransformed products of the progesterone. Subsequently, the chemical structures were subjected to an evaluation of their pharmacokinetic and toxicological properties and, with the application of ADMET filters. Results: Androstenedione, 17α-hydroxyprogesterone, and dihydrotestosterone, obtained by the process of biotransformation of PR by different filamentous fungi, showed good pharmacokinetic profiles and low toxicity compared to the control groups. The in-silico data associated with molecular docking studies revealed the best binding affinity and similarity in the interactions of these molecules against the human progesterone receptor target. Thus, the results of biological activity spectrum prediction highlight the great potential to investigate the role of molecular descriptors in the attribution of anti-cancer activities. Conclusions: The biocatalytic process, by filamentous fungi, can provide important molecules as a product of progesterone biotransformation, such as androstenedione, 17α-hydroxyprogesterone, and dihydrotestosterone. In this study we showed that these molecules have good pharmacokinetic profiles and low toxicity for antineoplastic activity (breast cancer).

Deciphering Human Emotions in Audio with Deep Learning Networks and MFCC Spectrogram Features

Introduction: Emotions play a fundamental role in human communication and interaction. In this paper, an effective and highly accurate sequential Deep Learning (DL) based emotion recognition model is proposed. Objectives: The main contribution of this work is to use effective Feature Extraction, Feature Selection (FS) and classification techniques so that accuracy of the model is increased while its computational complexity is reduced. Methods: The research utilizes a Surrey Audio-Visual expressed Emotion (SAVEE) dataset of audio recordings capturing emotional states. Feature extraction techniques are applied to obtain representative features from the audio signals. Specifically, Mel Spectrograms are computed and transformed into the decibel scale to capture the frequency content of the signals. These features are then fed into DL architecture for extracting relevant and important features. Moreover, Infinite Feature Selection (IFS) and Fuzzy based techniques are implemented for selecting only important and crucial features from the given feature set. For classification phase, a sequential DL based layered architecture is implemented for detecting and classifying emotions in audio signals. Results: The inclusion of convolutional layer with larger filter sizes and application of feature selection techniques improved the performance and interpretability of the approach. Conclusions: Through extensive experiments, an accuracy of 90.27% is achieved by proposed model on test dataset, outperforming traditional QPSO, WQPSO and PQPSO models by 33.027%, 31.817% and 30.897% respectively, and showcasing the potential of proposed model.

Switching from perfect absorption to plasmon induced transparency in graphene-vanadium dioxide metamaterials with high sensitivity

Abstract A multifunctional metamaterial, composed of patterned graphene layer atop the dielectric and vanadium dioxide (VO2) layer has been proposed. It can switch from a perfect absorption to plasmon induced transparency (PIT) as the VO2 transits from the metallic state into the dielectric state. By adjusting graphene’s Fermi energy, the perfect absorption can be finely tuned from a single-band to a dual-band. PIT occurs when the VO2 is in the dielectric state and the transparency window blue-shifts with a higher Fermi energy. Notably, it demonstrates the sensing ability and the resonant peaks embody a linear tendency with different substrates or embedding solutions. Thus, our findings not only provide new perspectives on the innovative design of multifunctional metamaterials but also underscore their potential applications in filters, optoelectronic detectors, and environmental monitors.

Impact of Artificial Intelligence (AI) Image Enhancing Filters on Patient Expectations for Plastic Surgery Outcomes.

Artificial intelligence (AI) technologies use a three-part strategy for facial visual enhancement: (1) Facial Detection, (2) Facial Landmark Detection, and (3) Filter Application (Chen in Arch Fac Plast Surg 21:361-367, 2019). In the context of the surgical patient population, open-source AI algorithms are capable of modifying or simulating images to present potential results of plastic surgery procedures. Our primary aim was to understand whether AI filter use may influence individuals' perceptions and expectations of post-surgical outcomes. We utilized Amazon's Mechanical Turk platform and collected information on prior experience using AI-driven visual enhancement. The cohort was divided into two groups: AI-exposed and non-AI-exposed. Questions gauged confidence in plastic surgery's ability to meet participant expectations. A second survey exposed users to either AI-enhanced or to unenhanced pre-operative photographs. Then, unedited post-operative photographs were shown and surgery's ability to enhance appearance was assessed. A multivariable linear analysis was constructed to measure associations between exposure to AI enhancement and survey outcomes. A total of 426 responses were analysed: 66.9% with AI exposure and 33.1% with no prior exposure. Participants with previous experience using AI-driven enhancers had a significantly higher average score for expectations after plastic surgery (P < 0.001). This finding was true across all outcomes, including surgery's ability to relieve discomfort with appearance/self-esteem (P < 0.001), to avoid post-operative complications (P < 0.001), to decrease post-operative scarring (P < 0.001), and to improve overall appearance (P < 0.001). The image comparison survey revealed that post-operative images were viewed as more successful at improving appearance when no pre-operative filter was applied (P = 0.151). Exposure to AI photograph enhancement may significantly raise expectations for plastic surgery outcomes and may predispose to having lower satisfaction after surgery. The significance of this study lies in its potential to reveal the extent to which AI technologies can shape patient understanding of their plastic surgery outcomes. Plastic surgeons aware of the effect of AI enhancement may consider using these results to guide counselling. his journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Nano‐Porous Melt‐Blown Poly(Lactic Acid) Fiber Mat Air Filters for High Efficiency Particulate Capture

AbstractThis study introduces a two‐step technique for developing nano‐porous, compostable melt‐blown nonwovens with high porosity, specifically engineered for high‐performance particulate capture in air filter applications. The first step entails creating a high melt flow index material by melt‐blending low‐viscosity polylactic acid (PLA) with a sacrificial additive, polyethylene glycol (PEG), of varying molecular weights. Rheological, compatibility, and thermal analyses are conducted on the sample blends. The MFI of the resulting blends ranges from 56 g/10 min (baseline PLA) to 238 g/10 min (PLA/PEG 400–10%), confirming their suitability for the melt‐blowing process. These blends are then formed into nonwoven mats using a twin‐screw extruder, producing microfibers with diameters between 1.05 and 2.64 µm. The second step involves boiling water etching to remove PEG, creating nanopores within the fibers. This etching process leaves a network of nanopores (50–200 nm in size), distributed throughout the microfiber structure. The PLA/PEG 2000 sample exhibits optimal properties, achieving ≈72% particulate capture efficiency for 0.3 µm NaCl particulates during air filtration testing. This study represents an innovative and eco‐friendly approach for creating nano‐porous nonwoven mats with potential applications in air filtration, water filtration, and battery separators, where high porosity is beneficial.

Developing a Third-Party API to Enhance Image Documents at the University of Mosul Data Center

Data integration plays a crucial role in SWE as it guarantees the proper functioning of multiple system, applications, and datasets. This integration is merit since it helps in developing a single reliable source of information from many, and the lack of data integration results in inefficient and time-consuming manual operations such as redundant data input, document retrieval issues, and data handling mistakes. This paper presents a comprehensive methodology for enhancing document images of Mosul University data centers and their quality assessment through integration with a cloud platform. The proposed approach consists of two main stages: the integration stage and the cloud platform stage. During the integration stage, the university's data center is seamlessly connected to a cloud platform via a RESTful API, allowing for efficient data interchange while maintaining data integrity and security. Next, image document representations are received in the cloud platform stage and undergo enhancement processes with specific tools and algorithms designed for image enhancement. The approaches employed in this study encompass image scaling, similarity evaluation utilizing the Histogram of Oriented Gradients (HOG) algorithm, image warping employing the FLANN (Fast Library for Approximate Nearest Neighbors) algorithm, and image quality enhancement by the application of a Laplacian sharpening filter. The proposed integrated cloud-based document enhancement has shown exceptional efficiency in data sharing and precise analysis and enhancement of picture documents within the university's data center.

Application of adaptive particle filter to shipborne cold atom gravimetry

Application of adaptive particle filter to shipborne cold atom gravimetry

Natural Zeolite Filled Recycled PVDF Filters Production and Application for Gray Water Treatment

In this study, Clinoptilolite (Clp) doped Poly(vinylidene fluoride) (PVDF) membranes (1-4 wt%) were produced by electrospinning method. Filtration tests of the simulated gray water components were investigated. Methylene blue (MB),...

SH1 mode plate wave resonator on −27°YX LiTaO3 single crystal plate with phase velocity of 12 km s−1

Abstract This study presents a first shear horizontal (SH1) mode plate acoustic wave resonator on a −27°YX LiTaO3 (LT) thin plate. The SH1 wave is generated when a backside electrode is formed on the opposite side of an interdigital transducer (IDT). Two resonators, one with Cu and the other with an Al backside electrodes, along with a Cu IDT were simulated and fabricated. Both fabricated SH1 resonators exhibited high phase velocities of 12 km s−1 and bandwidth (BW) of around 4.5%. The temperature coefficient of frequency is −38 ppm °C−1 at resonance and −56 ppm °C−1 at anti-resonance on a 0.13λ thick LT plate. Although the backside electrode is necessary for high coupling of the SH1 wave, it increases the clamp capacitance of the devices, resulting in low impedance characteristics of around 10 Ω and low impedance ratios around 30 dB. Additionally, the suppression of spurious responses is necessary for filter application.

I/O SNR and Noise Covariances Norm Ratio Relation in Kalman Filter

Kalman filters are used with great success to solve filtering problems in many fields of science and engineering. The ignorance of state noise covariance or the measurement noise covariance often creates difficulties in the practical application of Kalman filters. In this paper, the relation between the Input/Output signal-to-noise ratio (I/O SNR) and the noise covariance norm ratio for the discrete-time steady-state Kalman filter is established. The state or measurement noise covariance can be tuned via the I/O SNR. This result can be applied in time-varying systems and in steady-state systems, without the a priori knowledge of the state or measurement noise covariance.

New Wine in Old Bottles: The Sustainable Application of Slow Sand Filters for the Removal of Emerging Contaminants, a Critical Literature Review

The current treatment of wastewater has unintended negative environmental impacts. Conventional methods frequently involve the use of harmful chemicals, generate disinfectant by-products, consume significant amounts of energy, and produce wastes requiring additional efforts for safe disposal. Water stress exacerbated by contaminants of emerging concern (CECs) and climate change, is further straining aging treatment systems. A slow sand filter (SSF), with ligno-cellulosic layers, offers a novel, promising, and economic alternative for wastewater reclamation. This review examines the key SSF characteristics, obtained from recent studies, and explores the use of sustainable materials such as ligno-cellulose, as a treatment companion. The optimal SSF design includes a bed depth of &gt;0.6 m, particle effective size (D10) between 0.15 mm and 0.40 mm, and a uniformity coefficient (CU grain size ratio) of ≤2.0. It is established that SSF’s characteristic biolayer of microorganisms enhances contaminant removal via biodegradation. While biofilm-based removal of micropollutants is a proven mechanism, further research is needed to address CEC challenges. For example, the inclusion of sawdust in SSF filter layers can reduce energy consumption compared to conventional methods and can be recycled through thermal conversion, aligning with circular economy principles. This approach has the potential to improve wastewater treatment in emerging economies, contributing to the achievement of the UN Sustainability Goals.

Analysis of the Effectiveness of Using Active Filters and Passive Filters in Reducing Harmonics Using a Power Simulator (PSIM) at PT. Delta Jaya Mas

The Extruder Machine at PT. Delta Jaya Mas plays a critical role in the production of rubber and PVC hoses, as well as molded rubber products. This process involves the use of a 3-phase synchronous motor and Variable Frequency Drive (VFD), which can introduce harmonics into the electrical system due to their non-linear load. These harmonics can cause waveform distortion, leading to decreased efficiency and potential equipment damage. To overcome this issue, a comprehensive research is being conducted utilizing the Power Simulator (PSIM) program to analyze the impact of harmonics and evaluate the effectiveness of both active and passive filters in reducing these unwanted distortions. Initial assessments of the Extruder SDP panel revealed elevated levels of Total Harmonic Distortion (THD) in the current phases, while the voltage phases remained within acceptable thresholds. Simulation results demonstrated that the active filter was able to significantly reduce THD to an impressive 1.57%, showcasing its effectiveness in harmonic mitigation. On the other hand, the passive filter also proved beneficial, albeit to a slightly lesser extent, achieving a reduction in THD to 34.07%. This ongoing research not only aims to assist PT. Delta Jaya Mas in implementing appropriate harmonic reduction measures for their Extruder Machine but also seeks to advance the understanding of harmonic analysis and filter applications.

Coalescence performance of Janus filter for the removal of oil mist

The wettability has an important effect on coalescence performance of filter. In this paper, we utilized a commercial glass fiber filter as the substrate to prepared the Janus filter through dip-coating and electrospraying. The Janus filter exhibited a remarkable reduction in pressure drop, with only 2.8 kPa of wet pressure drop compared to 6.1 kPa for the original filter and 5.4 kPa for the oleophilic filter. Notably, the coating depth played a critical role in enhancing the coalescence performance of the Janus filter. Furthermore, when placing the Janus filter in reverse direction, its quality factor increased by 2.87 times, highlighting that orientation is an essential factor for coalescence performance of Janus filters. The results indicate great potential in the application of Janus filters for coalescence filtration.

Eliminating noise from a speech signal based on a pair of filters

Today, the increase in the number of devices working based on speech interfaces increases the importance of speech quality. However, even a minimal noise level can seriously affect the accuracy of speech signal recognition and processing. Therefore, denoising speech signals is an important task in signal processing, and it also serves to improve speech quality in telecommunications, speech recognition systems, and other speech-related applications. Applying existing noise reduction filters separately may not always be effective. Therefore, in this research, a noise reduction approach based on the sequential application of filters is proposed. Based on literature analysis, filters such as low-pass, band-pass, Kalman, Butterworth, and elliptic filters were selected, and pairs were formed based on them. Pairs of filters were applied to speech signals with different levels of noise, and the resulting filtered speech signals were evaluated based on the PESQ evaluation criterion. The purpose of this study is to determine the optimal pair of filters that can minimize the impact of noise on the quality of speech signals using the PESQ evaluation criterion. The results of the experiments showed that it is optimal to use a pair of band-pass and Butterworth filters at a low level of noise, a pair of low-pass and elliptic filters at a medium level, and a pair of band-pass and elliptic filters at a high level of noise. The results obtained are important and practical in the development of other hybrid methods of noise reduction.

Modulação da Microbiota Intestinal por Intervenções Dietéticas: Impacto no Controle da Obesidade

This literature review investigated the relationship between gut microbiota and obesity, with an emphasis on the effects of different dietary interventions on microbiota modulation. The search was conducted in the PubMed database, considering articles published between 2019 and 2024, freely available and in full text. Using DECs/MESH descriptors and combinations of terms in English, such as "gut microbiota," "obesity," and "dietary interventions," 180 articles were initially found. After applying filters to restrict the terms to title and abstract, this number was reduced to 46 articles. With the application of free access and publication date filters, 22 articles were selected. From the full reading and independent analysis by three authors, 13 articles were selected to form the final basis of the review. The results indicated that diets rich in fiber, prebiotics, and polyphenols promote a positive microbial balance, while low-carbohydrate diets may compromise microbial diversity in the long term. Modulation of the microbiota emerges as a promising strategy for weight control and metabolic health, but further research is needed on the personalization of nutritional interventions based on the genetic and microbial profile of individuals.

Application of Adaptive Search Window-Based Nonlocal Total Variation Filter in Low-Dose Computed Tomography Images: A Phantom Study

Computed tomography (CT) imaging using low-dose radiation effectively reduces radiation exposure; however, it introduces noise amplification in the resulting image. This study models an adaptive nonlocal total variation (NL-TV) algorithm that efficiently reduces noise in X-ray-based images and applies it to low-dose CT images. In this study, an AAPM CT performance phantom is used, and the resulting image is obtained by applying an annotation filter and a high-pitch protocol. The adaptive NL-TV filter was designed by applying the optimal window value calculated by confirming the difference between Gaussian filtering and the basic NL-TV approach. For quantitative image quality evaluation parameters, contrast-to-noise ratio (CNR), coefficient of variation (COV), and sigma value were used to confirm the noise reduction effectiveness and spatial resolution value. The CNR and COV values in low-dose CT images using the adaptive NL-TV filter, which performed an optimization process, improved by approximately 1.29 and 1.45 times, respectively, compared with conventional NL-TV. In addition, the adaptive NL-TV filter was able to acquire spatial resolution data that were similar to a CT image without applying noise reduction. In conclusion, the proposed NL-TV filter is feasible and effective in improving the quality of low-dose CT images.

Observation of higher-order gyrotropic modes and energy transfer in cylindrical ferromagnetic nanodots-based square lattices

We investigated and reported on a wide variety of gyrotropic modes in both isolated thick cylindrical magnetic vortices and lattices of such magnetic vortices arranged in square-lattice structures. In significantly thicker magnetic vortices, we see a higher-order flexure modes in addition to the uniform gyrotropic modes. We thoroughly examined the variation of the frequency and intensity of these modes with the thickness of the magnetic nanodots forming a vortex. We specifically looked at how the thickness, diameter, and aspect ratio of a single magnetic vortex and magnetic vortex lattice in various configurations affect the mode frequencies. When the magnetic vortex at the center is excited by an a.c. spin-polarized current with an excitation frequency equal to the collective gyrotropic frequencies of the lattice, we discuss the dynamics of the transfer of magnetic energy at three high symmetry points of the three-dimensional lattice of Permalloy nanodots with a vortex structure. The energy transmission in these lattice systems is estimated using the power and phase distributions for such configurations. Lattice configurations with substantially thicker nanodots consisting of uniform modes and higher-order flexure modes in the Eigen spectrum have transmission with uniform modes in specific directions and suppression in others, acting as a magnonic filter, and no transmission at all with higher-order modes. Such magnonic vortex lattice structures seek to improve the future prospects of technology and provide emerging applications in information processing devices, magnonic filters, three-dimensional waveguides, and magnonic crystals.