Advanced Filter Research Articles

A simple and efficient filter feature selection method via document-term matrix unitization

Text processing tasks commonly grapple with the challenge of high dimensionality. One of the most effective solutions to this challenge is to preprocess text data through feature selection methods. Feature selection can select the most advantageous features for subsequent operations (e.g., classification) from the native feature space of the text. This process effectively trims the feature space’s dimensionality, enhancing subsequent operations’ efficiency and accuracy. This paper proposes a straightforward and efficient filter feature selection method based on document-term matrix unitization (DTMU) for text processing. Diverging from previous filter feature selection methods that concentrate on scoring criteria definition, our method achieves more optimal feature selection by unitizing each column of the document-term matrix. This approach mitigates feature-to-feature influence and reinforces the role of the weighting proportion within the features. Subsequently, our scoring criterion subtracts the sum of weights for negative samples from positive samples and takes the absolute value. We conduct numerical experiments to compare DTMU with four advanced filter feature selection methods: max–min ratio metric, proportional rough feature selector, least loss, and relative discrimination criterion, along with two classical filter feature selection methods: Chi-square and information gain. The experiments are performed on four ten-thousand-dimensional feature space datasets: book, dvd, music, movie and two thousand-dimensional feature space datasets: imdb, amazon_cells, sourced from Amazon product reviews and movie reviews. Experimental findings demonstrate that DTMU selects more advantageous features for subsequent operations and achieves a higher dimensionality reduction rate than those of the other six methods used for comparison. Moreover, DTMU exhibits robust generalization capabilities across various classifiers and dimensional datasets. Notably, the average CPU time for a single run of DTMU is measured at 1.455 s.

Fault Diagnosis and Detection for Battery System in Real-World Electric Vehicles Based on Long-Term Feature Outlier Analysis

Accurate detection and diagnosis battery faults are increasingly important to guarantee safety and reliability of battery systems. Developed methods for battery early fault diagnosis concentrate on short-term data to analyze the deviation of external features without considering the long-term latent period of faults. This work proposes a novel data-driven method to detect long-term latent fault and abnormality for electric vehicles based on real-world operation data. Specifically, the battery fault features are extracted from the incremental capacity curves, which are smoothed by advanced filter algorithms. Secondly, principal component analysis algorithm is utilized to reduce dimensionality and the cumulative percent variance is to determine the number of significant features. Based on the features, a cluster algorithm is employed to capture the battery potential failure information. Moreover, the cumulative root-mean-square-deviation is introduced to quantificationally analyze the degree of the battery failures using large-scale battery data to avoid the missing fault reports using short-term data. In addition, a battery system failure index is proposed to evaluate battery fault conditions. The results indicate that the proposed long-term feature analysis method can effectively detect and diagnose faults.

Adaptable intelligent filters based on nanotextured nonwoven membranes containing water-insoluble hydrogels

The work demonstrates, for the first time, thermo-responsive, water-insoluble, hydrogel-based, nano-fibrous filter media comprised of copolymers of N-isopropylacrylamide (NIPAM) and methyl methacrylate formed by electrospinning. Moreover, a comprehensive novel physical explanation of all aspects responsible for the physical mechanisms resulting in the thermo-responsive regulation of the water flow rate and an enhanced interception of nanoparticles by such filter membranes is given. They are the wettable-non-wettable transition, pore, and fiber-size changes, as well as a diminishing filter thickness at the lower critical solution temperature (LCST) of the copolymers developed here, which interplay with a significant reduction in the water viscosity with temperature. Poly(N-isopropylacrylamide) (PNIPAM) hydrogel is an attractive material because of its thermo-responsive properties. Its wettability changes with water temperature. This characteristic holds great promise for the development of advanced filter media and related responsive materials. In this study, PNIPAM hydrogels were designed and transformed into filter membranes for applications in water filtration in biomedical and other related systems. These thermo-responsive filter membranes offer the potential for enhanced filtration efficiency, selectivity, and the overall system performance. Here, two different procedures were adopted to form water-insoluble thermo-responsive filter media based on PNIPAM hydrogels. The PNIPAM-based hydrogels were electrospun, resulting in the formation of thermo-responsive water-insoluble nanofiber membranes. These membranes underwent a series of comprehensive experiments to assess their performance and characteristics, including mass loss, water droplets for the wettability assessment, filtration tests, shrinkage measurements, and microscopic observations. These diverse experiments yield a full understanding of the PNIPAM-based nanofiber membranes’ properties and their potential applications.

Inferior Vena Cava Filter Retrieval: Simple to Complex.

Retrievable inferior vena cava filters were designed to provide mechanical prophylaxis from pulmonary embolism with the intent for retrieval once no longer indicated, and have been utilized at a high rate since their introduction. Unfortunately, retrieval rates have historically lagged behind, in part due to significant rates of failed standard retrieval techniques for filters with prolonged dwell time. Refinement in advanced retrieval techniques has now allowed (in experienced centers) for safe removal of filters previously considered irretrievable. An individualized approach is necessary for each potential advanced filter retrieval to determine appropriate course of action. This review will emphasize complex filter retrieval techniques amidst a larger discussion of inferior vena cava filters and their management.

Are the Noah and Noah-MP land surface models accurate for frozen soil conditions?

The accurate prediction of frozen soil conditions is a critical component of dynamic terrain characterization for engineering operations in cold regions. Currently the trusted resource for dynamic terrain within the US military uses gridded global soil conditions (i.e., temperature and moisture) generated by the Land Information System (LIS) architecture. LIS combines static terrain data with trusted surface weather information into the Noah land surface model to generate a continuous representation of the global surface conditions. Both the input weather data and the model output land surface state are adjusted to match the observed state using an advanced ensemble Kalman filter data assimilation technique to incorporate in situ and remote sensing observations. While this product has been robustly evaluated under unfrozen conditions only sporadic studies have investigated its ability to accurately simulate freeze/thaw cycles, and soil temperature and moisture under frozen conditions. Here, we present a broad comparison and evaluation of the simulated soil state against an in situ network of soil temperature and moisture distributed across the Continental United States that focuses on the winter season. This comparison includes both the Noah and the newer Noah with multi-parameterization (Noah-MP) land surface models. A comparison of the two models reveals substantial differences in simulated soil moisture, frozen content, and frozen soil strength. In general, Noah predicts colder soils with deeper frost depths, and frozen soils that are slightly stronger than Noah-MP. Initial results evaluating Noah and Noah-MP against in situ observations show that both models have significantly degraded model performance for frozen soil conditions compared to their performance for unfrozen soil. In particular, Noah has a significant and persistent cold bias of approximately −3 K when the soil temperature is below freezing. This cold bias is not present in Noah-MP and further analysis shows that this improvement was almost entirely caused by a better representation of thermal conduction through snow in Noah-MP. Specifically, we find that the bulk effective thermal conductivity of a snowpack is 2× greater in Noah, which allows for greater surface heat loss during the winter and, consequently, lower soil temperatures. This analysis reveals that the more advanced multi-layer snow model in Noah-MP substantially improves the representation of soil temperature during the cold season.

Tri-layer gradient structured micro/nanofibrous nonwovens for high filtration efficiency and low air resistance

The coexistence of PM2.5 pollution and the ongoing pandemic poses significant risks to human health. Protective masks incorporating high-efficiency particulate air filters offer adequate protection against PM2.5 particles. Micro/nanofibrous nonwovens, including melt-blown and electrospun nonwovens, are essential filter materials. This study focuses on the filtration mechanism and geometric structure design of multi-level structured micro/nanofibrous nonwovens. A comprehensive investigation was conducted on a filter core material composed of polyacrylonitrile, a polystyrene electrospun membrane, a polypropylene (PP) melt-blown membrane, and a supporting outer layer of a PP spun-bonded membrane. The resulting nonwovens exhibited exceptional filtration efficiency of 99.98% for PM0.3 particles, with a low pressure drop of 60 Pa at 32 L/min inlet air velocity. Filtration efficiencies of 99.99% and 100% were achieved for PM1.0 and PM2.5 particles, respectively. These characteristics make the designed composite nonwovens a very promising filter material for masks. The study contributes to understanding filtration mechanisms and developing advanced high-efficiency filter materials, enhancing protection against airborne pollutants.

Random Forest Machine Learning for Spam Email Classification

This research discusses the crucial role of email as a main element in digital communication, facilitating information transfer and serving as an advertising platform. However, the problem of email spam, which involves sending unsolicited commercial messages, has had negative impacts such as consuming large amounts of resources and disrupting user experience. With its affordable cost and ease of sending messages to thousands of recipients, email spam includes product promotions, pornographic material, viruses and irrelevant content. The impact includes loss of time and damage to the user's computer resources. To address this problem, email services provide advanced spam filters that use email content analysis and machine learning techniques. This research focuses on the use of the Random Forest Classification algorithm as a basis for filtering spam emails. Although Random Forest is known to have strong classification capabilities, the risk of overfitting is a challenge. Therefore, this study adopts the Randomized Search CV method to identify the best parameter combination, ensuring the reliability of the model in dealing with the complexity of diverse email datasets. With this approach, this research contributes to the development of effective solutions to reduce the impact of email spam in digital communications.

Optimizing Controls to Track Moving Targets in an Intelligent Electro-Optical Detection System

Electro-optical detection systems face numerous challenges due to the complexity and difficulty of targeting controls for “low, slow and tiny” moving targets. In this paper, we present an optimal model of an advanced n-step adaptive Kalman filter and gyroscope short-term integration weighting fusion (nKF-Gyro) method with targeting control. A method is put forward to improve the model by adding a spherical coordinate system to design an adaptive Kalman filter to estimate target movements. The targeting error formation is analyzed in detail to reveal the relationship between tracking controller feedback and line-of-sight position correction. Based on the establishment of a targeting control coordinate system for tracking moving targets, a dual closed-loop composite optimization control model is proposed. The outer loop is used for estimating the motion parameters and predicting the future encounter point, while the inner loop is used for compensating the targeting error of various elements in the firing trajectory. Finally, the modeling method is substituted into the disturbance simulation verification, which can monitor and compensate for the targeting error of moving targets in real time. The results show that in the optimal model incorporating the nKF-Gyro method with targeting control, the error suppression was increased by up to 36.8% compared to that of traditional KF method and was 25% better than that of the traditional nKF method.

Dynamically tuning and reconfiguring microwave bandpass filters using optical control of switching elements

AbstractWe report a novel approach for dynamically tuning and reconfiguring microwave bandpass filters (BPFs) based on optically controlled switching elements using photoconductivity modulation in semiconductors. For a prototype demonstration, a BPF circuit featuring a second‐order design using two closely coupled split‐ring resonators embedded with multiple silicon chips (as switching elements) was designed, fabricated, and characterized. The silicon chips were optically linked to fiber‐coupled laser diodes (808 nm light) for switching/modulation, enabling dynamic tuning and reconfiguring of the BPF without any complex biasing circuits. By turning on and off the two laser diodes simultaneously, the BPF response can be dynamically reconfigured between bandpass and broadband suppression. Moreover, the attenuation level of the passband can be continuously adjusted (from 0.7 to 22 dB at the center frequency of 3.03 GHz) by varying the light intensity from 0 to 40 W/cm2. The tuning/reconfiguring 3‐dB bandwidth is estimated to be ~200 kHz. In addition, the potential and limitations of the proposed approach using photoconductivity modulation are discussed. With the strong tuning/reconfiguring capability demonstrated and the great potential for high‐frequency operation, this approach holds promise for the development of more advanced tunable filters and other adaptive circuits for next‐generation sensing, imaging, and communication systems.

The Efficiency of a Biological Reactor in a Domestic Wastewater Treatment Plant Operating Based on ABS (Acrylonitrile Butadiene Styrene) Material and Recycled PUR (Polyurethane) Foam

The primary objective of this research was to assess the efficacy of a novel solution under conditions closely resembling those of real-world scenarios. Biological beds, or filters, hold significant potential for widespread implementation in individual households, particularly in areas with dispersed housing. The system’s aim was to improve the quality of wastewater treated in on-site domestic biological treatment plants. A pivotal aspect of the project involved developing a prototype research installation for conducting comprehensive testing. Our installation system consisted of several components designed to create a laboratory-scale model for domestic wastewater treatment. The model comprised four biological reactors filled with ABS material and secured by a PUR frame. Additionally, the tested model included a controller for wastewater dosing control, a septic tank as a reservoir, and four tanks for collecting purified wastewater. Through regression analysis using the Generalized Linear Model (GLM), a correlation between CODCr and TSS was revealed. This study presents the research findings concerning the development of a prototype installation that incorporates an advanced reactor or filter. The data derived from this research have the potential to contribute to the creation of products that enhance the performance and efficiency of household wastewater treatment systems.

Gelatin-reinforced cellulose nanofiber composite cryogels for effective separation of small particulate matter in air

Herein, novel composite aerosol filters solely comprising renewable materials were designed using gelatin as a reinforcement agent and aqueous tert-butyl alcohol (TBA) as a dispersing medium for the cellulose nanofiber skeleton. The prepared composite cryogels exhibited distinct spider-web-like crosslinked structures and nanoporous architectures with porosity exceeding 98.8 % and density of less than 0.018 g/cm3. These composite filters exhibited high filtration efficiency and a robust and flexible mechanical structure due to gelatin reinforcement; furthermore, their quality factor exceeded the target threshold of 0.01 Pa−1 for 300 nm particle size. The filtration performance and mechanical properties of the composite filters could be tailored by adjusting the cellulose nanofiber (CNF), gelatin, and TBA contents, enabling the preparation of cryogels with a firm and strong structure. This study introduces ultraporous solids based on nanocellulose (NC) cryogels, which are promising, novel, and green materials for the production of advanced high-performance filter media for aerosol separation. Furthermore, the aforementioned approach is promising for the preparation of mechanically robust nanoporous biomaterials suitable for diverse applications such as heat and thermal insulation.

A Systematic Review and Meta-Analysis of the Outcomes of Laparoscopic Cholecystectomy Compared to the Open Procedure in Patients with Gallbladder Disease.

Background Conflicting evidence regarding the laparoscopic versus open cholecystectomy outcomes in scientific literature impacts the medical decision-making for patients with gallbladder disease. This study aimed to compare a range of primary and secondary outcomes between patients receiving laparoscopic cholecystectomy and those with open intervention. Methods Articles published from 1993 to 2023 were explored by utilizing advanced filters of PubMed Central/Medline, Web of Science, CINAHL, JSTOR, Cochrane Library, Scopus, and EBSCO. The gallbladder disease was determined by the presence of one or more of the following conditions: 1) Gangrenous cholecystitis, 2) acute cholecystitis, 3) chronic gallbladder diseases, and 4) cholelithiasis. The primary end-point was mortality, while the secondary outcome included (1) bile leakage, 2) common bile duct injury, 3) gangrene, 4) hospital stay (days), 5) major complications, 6) median hospital stay (days), (7) pneumonia, 8) sick leaves (days), and 9) wound infection. Results Statistically significant reductions were observed in mortality (odds ratio [OR]: 0.30, 95% confidence interval [CI]: 0.30, 0.45, p < 0.00001), mean hospital stay duration (mean difference: -2.68, 95% CI: -3.66, -1.70, p < 0.00001), major complications (OR: 0.35, 95% CI: 0.19, 0.64, p = 0.0005), post/intraoperative wound infection (OR: 0.29, 95% CI: 0.16, 0.51, p < 0.0001), and sick leaves (OR: 0.34, 95% CI: 0.14, 0.80, p = 0.01) in patients who underwent laparoscopic cholecystectomy compared with those with the open intervention. No statistically significant differences were recorded between the study groups for bile leakage, common bile duct injury, gangrene, median hospital stay days, and pneumonia ( p > 0.05). Conclusions The pooled outcomes favored the use of laparoscopic cholecystectomy over the open procedure in patients with gallbladder disease. The consolidated findings indicate the higher impact of laparoscopic cholecystectomy in improving patient outcomes, including safety episodes, compared with open cholecystectomy.

A Convolutional Framework for Color Constancy.

We introduce a convolutional framework (CF) for computational color constancy, building upon the established low-level image feature-based framework, which utilized simple image statistics for illuminant estimation. Our framework expands upon this through an end-to-end learnable neural architecture. This adaptation enables the learning and usage of advanced filters that are not restricted to Gaussian kernels operating on individual color channels, thus generalizing the capabilities of the original framework. Additionally, our general framework supports deeper convolutional architectures, thus increasing its computational power. It can also be efficiently applied to estimate multiple spatially varying illuminants within a single scene. Our experimental results on standard datasets demonstrate that the CF outperforms the best methods in the low-level framework, improving the illuminant estimation accuracy by up to 34% for single illuminant estimation and 30% for multiple illuminants estimation. Additionally, our framework exhibits superior performance even when the number of training images is reduced. Finally, we document the inference speedup of our implementation reaching up to 30 × , making the CF especially suitable for applications where efficiency is critical. Source code and trained models available at: https://github.com/MarcoBauzz/convolutional-color-constancy.

Facile preparation of durable superhydrophobic DTMS@HKUST-1 wood membrane for continuous oil-water separation in harsh conditions

Manufacturing durable wood membranes that demonstrate both high efficiency and ultra-high flux for continuous oil-water separation utilizing scalable and cost-effective processes remains a serious challenge. In this study, a facile two-step approach is employed to prepare a novel superhydrophobic/oleophilic wood-based MOF (metal-organic framework) membrane. This method involves in-situ anchoring of the octahedral Cu-HKUST-1 (copper benzene-1,3,5-tricarboxylate) onto the wood sponge (WS), which inherently features internal micro-channels. The structure is then modified using the eco-friendly hydrophobic long-chain coupling agent dodecyltrimethoxysilane (DTMS). Due to its unique micro/nanoscale rough surface morphology and low surface energy, the functional wood membrane (DTMS/HKUST-1@WS) exhibited a high contact angle of 162.98±1.10° for water and was able to effectively separate various immiscible oil/water mixtures. Notably, the DTMS/HKUST-1@WS membrane can be integrated into a pump drive system, serving as an advanced filter element for the sustained and efficient oil-water constituents. Results showed that this membrane possesses attractive permeability toward CCl4 (56689.34 L·m−2·h−1) and toluene (47241.12 L·m−2·h−1), achieving separation efficiencies over 99.0%. Furthermore, this novel membrane exhibits remarkable resistance to physical damage, high/low temperatures, and corrosive solutions, thereby showcasing stable superhydrophobic and superoleophilic interfaces suitable for oily wastewater treatment in harsh environments. Thus, this work offers an innovative approach for designing functional wood-based MOF membranes to achieve highly efficient continuous oil-water separation.

Divertor Thomson Scattering on Globus-M2

We present the first Thomson scattering (TS) measurements of electron temperature and densityin the lower divertor of the Globus-M2 tokamak. The divertor TS diagnostics is designed for local measurementsof the Te(z, t) in the range of 1–100 eV and ne(z, t) in the range of m–3. Parameters of theprobing Nd:YAG laser are as follows 1064 nm/2 J/100 Hz/3 ns. The probing chord is launched vertically atR = 24 cm and covers areas of the inner leg, vicinity of separatrix and private flux region. Along probing chordof 110 mm, 9 spatial points were realized. Advanced filter polychromators were used to analyze Thomsonscattering spectra.

Improvement of an extended ensemble coupled data Assimilation–Forecast system and its application in El Niño diversity predictions

The El Niño–Southern Oscillation (ENSO) can cause climate anomalies on a global scale, and further affect human life and activities in coastal zones. Therefore, its forecast is of great significance for early disaster warning and coastal management. However, the frequent occurrence of central Pacific (CP) El Niño events increases the diversity and complexity of ENSO, severely reducing its prediction efficiency. In this study, an extended ensemble coupled data assimilation–forecast system was employed to investigate the prediction of different types of El Niño events, including eastern Pacific (EP) and CP events. The extended system was based on the fifth-generation Lamont–Doherty Earth Observation (LDEO5) model, in which an advanced ensemble Kalman filter was used to construct a multisource data assimilation system, and a stochastic optimal method was used to measure the influence of atmospheric stochastic processes on model prediction errors. The extended system was used to predict two types of El Niño events that occurred between January 1950 and December 2018. The results showed that the extended system was generally able to predict EP events with a higher accuracy than CP events for all lead times. The extended system successfully predicted the mature phase of EP events up to 12 months in advance but could only predict the mature phase of CP events up to 6 months in advance. The extended system was also able to depict the evolution of both EP and CP events, although the sea surface temperature anomalies were underestimated. The extended system not only provides a useful platform for improving ENSO prediction accuracies in association with El Niño diversity but also provides an important tool for disaster early warning and coastal management.

An advanced median filter for improving the signal-to-noise ratio of seismological datasets

Strong noise usually disturbs the recorded seismic waves, resulting in low-quality seismic data sometimes with an extremely low signal-to-noise ratio (S/N), which negatively impacts the subsequent seismological processes, e.g., imaging, inversion, and interpretation. Suppressing undesirable noise is a meaningful procedure to increase the S/N of seismological datasets. This issue can be partially addressed using the recently proposed structure-oriented space-varying median filter (SOSVMF) by filtering out the unwanted noise from noise-corrupted signals. However, the SOSVMF approach has limitations when the raw data is corrupted by various sources of noise which is more common in practice. To conquer the difficulties in improving the S/N in the face of diverse types of strong noise, we introduce MATamf, an open-source MATLAB code package based on a novel advanced median filter (AMF). The proposed AMF workflow takes advantage of multiple denoising operators, i.e., the bandpass (BP) filter, SOSVMF, dip filter in the frequency–wavenumber (FK) domain, Curvelet, and local orthogonalization (LO), all in the same framework to simultaneously attenuate all types of noise and improve S/N. To demonstrate the usefulness of the MATamf package and its advantage over conventional denoising methods, we introduce the principles behind the proposed AMF framework and present results from a variety of seismological problems, including seismic reflection, distributed acoustic sensing (DAS), as well as global seismological tasks of receiver function and SS-precursors imaging. All results show that the proposed AMF workflow can be conveniently utilized to improve the S/N of a wide spectrum of seismological applications.

A Nanostructured Protein Filtration Device for Possible Use in the Treatment of Alzheimer's Disease-Concept and Feasibility after In Vivo Tests.

Alzheimer's disease (AD), along with other neurodegenerative disorders, remains a challenge for clinicians, mainly because of the incomplete knowledge surrounding its etiology and inefficient therapeutic options. Considering the central role of amyloid beta (Aβ) in the onset and evolution of AD, Aβ-targeted therapies are among the most promising research directions. In the context of decreased Aβ elimination from the central nervous system in the AD patient, the authors propose a novel therapeutic approach based on the "Cerebrospinal Fluid Sink Therapeutic Strategy" presented in previous works. This article aims to demonstrate the laborious process of the development and testing of an effective nanoporous ceramic filter, which is the main component of an experimental device capable of filtrating Aβ from the cerebrospinal fluid in an AD mouse model. First, the authors present the main steps needed to create a functional filtrating nanoporous ceramic filter, which represents the central part of the experimental filtration device. This process included synthesis, functionalization, and quality control of the functionalization, which were performed via various spectroscopy methods and thermal analysis, selectivity measurements, and a biocompatibility assessment. Subsequently, the prototype was implanted in APP/PS1 mice for four weeks, then removed, and the nanoporous ceramic filter was tested for its filtration capacity and potential structural damages. In applying the multi-step protocol, the authors developed a functional Aβ-selective filtration nanoporous ceramic filter that was used within the prototype. All animal models survived the implantation procedure and had no significant adverse effects during the 4-week trial period. Post-treatment analysis of the nanoporous ceramic filter showed significant protein loading, but no complete clogging of the pores. We demonstrated that a nanoporous ceramic filter-based system that filtrates Aβ from the cerebrospinal fluid is a feasible and safe treatment modality in the AD mouse model. The presented prototype has a functional lifespan of around four weeks, highlighting the need to develop advanced nanoporous ceramic filters with anti-biofouling properties to ensure the long-term action of this therapy.

An integrated approach of advanced methods for mapping geologic structures and sedimentary thickness in Ukelle and adjoining region (Southeast Nigeria)

High-resolution aeromagnetic data were enhanced using recent and advanced filters to map the geologic structures of the Ukelle and adjoin region (Southeast Nigeria). Aeromagnetic data were reduced to the equator (RTE) and upward continued to 100 m. Subsequently, enhancement operations like the tilt angle of the horizontal gradient (TAHG), logistic function of the horizontal gradient (LTHG), and fast sigmoid function (FSED) operations were carried out. The results from these filters indicated that the ENE-WSW, NE-SW, NNE-SSW, and NNW-SSE orientations dominate the structural pattern of the Ukelle region. In addition, the edge filters delineated NE-SW trending synclinal structures that match the location of thick (500-1400 m) sedimentation obtained by the tilt-depth (TD) method. Furthermore, the structural map obtained from remote sensing data validated the lineament orientations and position of the NE-SW trending synclinal structure. The results also showed that the study location’s southeastern and northwestern flanking portions, controlled by extensive Santonian igneous intrusions and metamorphisms, are characterized by high lineaments and thin (0-500 m) sedimentation. The observed thin sedimentation is believed to be caused by widespread Santonian tectonic events in the area. At the same time, related geologic structures served as migration pathways and accumulation zones for rift mineralization.

Observing atmospheric convection with dual-scanning lidars

Abstract. While convection is a key process in the development of the atmospheric boundary layer, conventional meteorological measurement approaches fall short in capturing the evolution of the complex dynamics of convection. To obtain deeper observational insight into convection, we assess the potential of a dual-lidar approach. We present the capability of two pre-processing procedures, an advanced clustering filter instead of a simple threshold filter and a temporal interpolation, to increase data availability and reduce errors in the individual lidar observations that would be amplified in the dual-lidar retrieval. To evaluate the optimal balance between spatial and temporal resolution to sufficiently resolve convective properties, we test a set of scan configurations. We deployed the dual-lidar setup at two Norwegian airfields in a different geographic setting and demonstrate its capabilities as a proof of concept. We present a retrieval of the convective flow field in a vertical plane above the airfield for each of these setups. The advanced data filtering and temporal interpolation approaches show an improving effect on the data availability and quality and are applied to the observations used in the dual-lidar retrieval. All tested angular resolutions captured the relevant spatial features of the convective flow field, and balance between resolutions can be shifted towards a higher temporal resolution. Based on the evaluated cases, we show that the dual-lidar approach sufficiently resolves and provides valuable insight into the dynamic properties of atmospheric convection.