The Atlanta auto repair company, Express Oil, knows that changing your oil every 3,000 miles is a great way to keep your vehicle running smoothly. Synthetic blends can last longer, but most oil filters are not designed to withstand longer intervals.

Preset (Everyday) + transform + exposure + graduated filter + radial filter. If shots like this take more than 2 minutes to edit, it’s probably not worth editing. ⏱
—
Boxing Day will be the last day to get my Lightroom presets discounted, which leaves you only 3 more days! Get on it! ???? (at Toronto, Ontario)

Una nueva herramienta salida del laboratorio de ideas de Google permite comparar de qué se habla en el mundo y en país concreto para analizar qué temáticas están infravaloradas en dicha zona.

NotePage, Inc. is pleased to announce the addition of RSSFilter to their popular RSS scripts directory.

RSSFilter is a PHP script that filters the contents of an RSS feed. The filters can be defined by the publisher so that the resulting RSS feed contains only the information they want. RSSFilter uses rules to filter the items that are included from a feed in the resulting feed.

RSSFilter is designed to be able to be used in conjunction with other RSS Scripts to provide additional RSS solutions. For example RSSFilter can be used with RSSmesh, so that mulitple feeds can be filtered and then merged into a single RSS feed.

RSSFilter is a PHP script that filters the contents of an RSS feed. The filters can be defined by the publisher so that the resulting RSS feed contains only the information they want. RSSFilter uses rules to filter the items that are included from a feed in the resulting feed.

RSS Filter

RSSFilter is a PHP script that filters the contents of an RSS feed. The filters can be defined by the publisher so that the resulting RSS feed contains only the information they want. RSSFilter uses rules to filter the items that are included from a feed in the resulting feed.

RSS Filter

The RSSFilter script is used to filter RSS feeds.

The RSSFilter script is used to filter RSS feeds.

RSS Filter

RSS Filter Script is a PHP script that filters the contents of an RSS feed. The filters can be defined by the publisher so that the resulting RSS feed contains only the information they want. RSSFilter uses rules to filter the items that are included from a feed in the resulting feed.

Fr. Emmanuel Kahn talks about two people who came into the Temple. Only one of them is to be admired.

Collaborative Filtering(CF) is a well-known technique in recommender systems. CF exploits relationships between users and recommends items to the active user according to the ratings of his/her neighbors. CF suffers from the data sparsity problem, where users only rate a small set of items. That makes the computation of similarity between users imprecise and consequently reduces the accuracy of CF algorithms. In this article, we propose a clustering approach based on the social information of users to derive the recommendations. We study the application of this approach in two application scenarios: academic venue recommendation based on collaboration information and trust-based recommendation. Using the data from DBLP digital library and Epinion, the evaluation shows that our clustering technique based CF performs better than traditional CF algorithms.

by Mitchell Longan[1] Introduction The European Parliament is currently considering a proposal to address problems of piracy and other forms of copyright infringement associated with the digital world.[2] Article 13 of the proposed Directive on Copyright in the Digital Single

In order to improve recommendation coverage, a personalised recommendation method for English online learning resources based on improved collaborative filtering algorithm is studied to enhance the comprehensiveness of personalised recommendation for learning resources. Use matrix decomposition to decompose the user English online learning resource rating matrix. Cluster low dimensional English online learning resources by improving the K-means clustering algorithm. Based on the clustering results, calculate the backfill value of English online learning resources and backfill the information matrix of low dimensional English online learning resources. Using an improved collaborative filtering algorithm to calculate the predicted score of learning resources, personalised recommendation of English online learning resources for users based on the predicted score. Experimental results have shown that this method can effectively backfill English online learning resources, and the resource backfilling effect is excellent, and it has a high recommendation coverage rate.

In order to overcome the problems of large error, low evaluation accuracy and long evaluation time in traditional evaluation methods of ideological and political education, this paper designs a quantitative evaluation method of ideological and political education achievements based on collaborative filtering algorithm. First, the evaluation index system is constructed to divide the teaching achievement evaluation index data in a small scale; then, the quantised dataset is determined and the quantised index weight is calculated; finally, the collaborative filtering algorithm is used to generate a set with high similarity, construct a target index recommendation list, construct a quantitative evaluation function and solve the function value to complete the quantitative evaluation of teaching achievements. The results show that the evaluation error of this method is only 1.75%, the accuracy can reach 98%, and the time consumption is only 2.0 s, which shows that this method can improve the quantitative evaluation effect.

Aim/Purpose: This study aims to devise a personalized solution for online healthcare platforms that can alleviate problems arising from information overload and data sparsity by providing personalized healthcare services to patients. The primary focus of this paper is to develop an effective medicine recommendation approach for recommending suitable medications to patients based on their specific medical conditions. Background: With a growing number of people becoming more conscious about their health, there has been a notable increase in the use of online healthcare platforms and e-services as a means of diagnosis. As the internet continues to evolve, these platforms and e-services are expected to play an even more significant role in the future of healthcare. For instance, WebMD and similar platforms offer valuable tools and information to help manage patients’ health, such as searching for medicines based on their medical conditions. Nonetheless, patients often find it arduous and time-consuming to sort through all the available medications to find the ones that match their specific medical conditions. To address this problem, personalized recommender systems have emerged as a practical solution for mitigating the burden of information overload and data sparsity-related issues that are frequently encountered on online healthcare platforms. Methodology: The study utilized a dataset of MC ratings obtained from WebMD, a popular healthcare website. Patients on this website can rate medications based on three criteria, including medication effectiveness, ease of use, and satisfaction, using a scale of 1 to 5. The WebMD MC rating dataset used in this study contains a total of 32,054 ratings provided by 2,136 patients for 845 different medicines. The proposed HSMCCF approach consists of two primary modules: a semantic filtering module and a multi-criteria filtering module. The semantic filtering module is designed to address the issues of data sparsity and new item problems by utilizing a medicine taxonomy that sorts medicines according to medical conditions and makes use of semantic relationships between them. This module identifies the medicines that are most likely to be relevant to patients based on their current medical conditions. The multi-criteria filtering module, on the other hand, enhances the approach’s ability to capture the complexity of patient preferences by considering multiple criteria and preferences through a unique similarity metric that incorporates both distance and structural similarities. This module ensures that patients receive more accurate and personalized medication recommendations. Moreover, a medicine reputation score is employed to ensure that the approach remains effective even when dealing with limited ratings or new items. Overall, the combination of these modules makes the proposed approach more robust and effective in providing personalized medicine recommendations for patients. Contribution: This study addresses the medicine recommendation problem by proposing a novel approach called Hybrid Semantic-based Multi-Criteria Collaborative Filtering (HSMCCF). This approach effectively recommends medications for patients based on their medical conditions and is specifically designed to overcome issues related to data sparsity and new item recommendations that are commonly encountered on online healthcare platforms. The proposed approach addresses data sparsity and new item issues by incorporating a semantic filtering module and a multi-criteria filtering module. The semantic filtering module sorts medicines based on medical conditions and uses semantic relationships to identify relevant ones. The multi-criteria filtering module accurately captures patient preferences and provides precise recommendations using a novel similarity metric. Additionally, a medicine reputation score is also employed to further expand potential neighbors, improving predictive accuracy and coverage, particularly in sparse datasets or new items with few ratings. With the HSMCCF approach, patients can receive more personalized recommendations that are tailored to their unique medical needs and conditions. By leveraging a combination of semantic-based and multi-criteria filtering techniques, the proposed approach can effectively address the challenges associated with medicine recommendations on online healthcare platforms. Findings: The proposed HSMCCF approach demonstrated superior effectiveness compared to benchmark recommendation methods in multi-criteria rating datasets in terms of enhancing both prediction accuracy and coverage while effectively addressing data sparsity and new item challenges. Recommendations for Practitioners: By applying the proposed medicine recommendation approach, practitioners can develop a medicine recommendation system that can be integrated into online healthcare platforms. Patients can then utilize this system to make better-informed decisions regarding the medications that are most suitable for their specific medical conditions. This personalized approach to medication recommendations can ultimately lead to improved patient satisfaction. Recommendation for Researchers: Integrating patient medicine reviews is a promising way for researchers to elevate the proposed medicine recommendation approach. By leveraging patient reviews, the approach can gain a more comprehensive understanding of how certain medications perform for specific medical conditions. Additionally, exploring the relationship between MC-based ratings using an improved aggregation function can potentially enhance the accuracy of medication predictions. This involves analyzing the relationship between different criteria, such as medication effectiveness, ease of use, and satisfaction of the patients, and determining the optimal weighting for each criterion based on patient feedback. A more holistic approach that incorporates patient reviews and an improved aggregation function can enable the proposed medicine recommendation approach to provide more personalized and accurate recommendations to patients. Impact on Society: To mitigate the risk of infection during the COVID-19 pandemic, the promotion of online healthcare services was actively encouraged. This allowed patients to continue accessing care and receiving treatment while adhering to physical distancing guidelines and shielding measures where necessary. As a result, the implementation of personalized healthcare services for patients is expected to be a major disruptive force in healthcare in the coming years. This study proposes a personalized medicine recommendation approach that can effectively address this issue and aid patients in making informed decisions about the medications that are most suitable for their specific medical conditions. Future Research: One way that may enhance the proposed medicine recommendation approach is to incorporate patient medicine reviews. Furthermore, the analysis of MC-based ratings using an improved aggregation function can also potentially enhance the accuracy of medication predictions.

Aim/Purpose: This study aims to analyze the factors that influence user addiction to AR face filters in social network applications and their impact on the online social anxiety of users in Indonesia. Background: To date, social media users have started to use augmented reality (AR) face filters. However, AR face filters have the potential to create positive and negative effects for social media users. The study combines the Big Five Model (BFM), Sense of Virtual Community (SVOC), and Stimuli, Organism, and Response (SOR) frameworks. We adopted the SOR theory by involving the personality factors and SOVC factors as stimuli, addiction as an organism, and social anxiety as a response. BFM is the most significant theory related to personality. Methodology: We used a quantitative approach for this study by using an online survey. We conducted research on 903 Indonesian respondents who have used an AR face filter feature at least once. The respondents were grouped into three categories: overall, new users, and old users. In this study, group classification was carried out based on the development timeline of the AR face filter in the social network application. This grouping was carried out to facilitate data analysis as well as to determine and compare the different effects of the factors in each group. The data were analyzed using the covariance-based structural equation model through the AMOS 26 program. Contribution: This research fills the gap in previous research which did not discuss much about the impact of addiction in using AR face filters on online social anxiety of users of social network applications. Findings: The results of this study indicated neuroticism, membership, and immersion influence AR face filter addiction in all test groups. In addition, ARA has a significant effect on online social anxiety. Recommendations for Practitioners: The findings are expected to be valuable to social network service providers and AR creators in improving their services and to ensure policies related to the list of AR face filters that are appropriate for use by their users as a form of preventing addictive behavior of that feature. Recommendation for Researchers: This study suggested other researchers consider other negative impacts of AR face filters on aspects such as depression, life satisfaction, and academic performance. Impact on Society: AR face filter users may experience changes in their self-awareness in using face filters and avoid the latter’s negative impacts. Future Research: Future research might explore other impacts from AR face filter addiction behavior, such as depression, life satisfaction, and so on. Apart from that, future research might investigate the positive impact of AR face filters to gain a better understanding of the impact of AR face filters.

This tutorial shows how to add/remove filter tag in collection pages in Shopify.

The post Shopify. How to add/remove tag filter in Collections pages appeared first on Template Monster Help.

To help you navigate the messy world of streaming services availability, Next Episode has a few new features: All lists (search, browse, top/discovery charts, watchlist, track episodes) now also show streaming availability You can now filter by streaming services availability In addition, you can add the streaming services you use to My Services and filter through that as wellThis is not just for US - your country should've been automatically detected, but if not - you can easily switch it here. Basically, it should now be easier than ever to find out where your stuff is streaming and find new great shows on the streaming services you use. Our movies section will get the same treatment soon. Let me know your thoughts and as always - if you appreciate my efforts here - consider getting Premium. Thank you!

I mentioned movies will get the streaming services availability - and it's now done. Here's what's new:streaming services availability was added in all Movies lists (my movies, browse, hot, trending, search)you can now filter these lists by streaming services or "my streaming services" availabilityAlong with these specific updates regarding the Movies section, there are a few general improvements as well:when filtered by a streaming service, what you filter by is first and highlighted - the rest is grayed outmany new countries are now supported so if yours wasn't there before - check again - it most likely is nowPlease let me know how these work for you and if something looks weird on your device/browser. Next - I plan to focus on improving how My Movies looks and works (both on desktop and mobile)

The Speedglas Auto-Darkening Filter Kit 9100XXi gives welders superior visibility in both dark and light states.

FREE 2.0 earbuds filter damaging noise levels while allowing users to listen to music and take phone calls without removing the earbuds.

This PID pre-filter tube is for selective determination of benzene in air containing a mixture of volatile organic compounds (e.g., toluene, xylene, ethyl benzene, octane, etc.).

A NIOSH-approved low-profile HEPA filter for CleanSpace2 respirators now is available.

Washington — Seal checks should be conducted every time respiratory protection is used on the job, and employers and workers should ensure the equipment is worn properly so an adequate seal is achieved, NIOSH states in a recently published list of frequently asked questions about user seal checks.

The unique acoustic behind the dual-filtered HearDefender-DF varies depending on the user’s individual noise exposure.

The HearDefenders-DF is the first and only product to offer the flexibility of two levels of hearing protection and the ability to hook up to radio communications.

How does the filter media in an N95 filtering facepiece respirator really work?

In this four-part series on dust collection for the food industry, we’ll cover everything you need to know about air filtration.

Learn how long cartridge filters last, how to know when they need to be changed and what you can do to extend their life and performance. 

Imaging energy filters in photoelectron microscopes and momentum microscopes use spherical fields with deflection angles of 90°, 180° and even 2 × 180°. These instruments are optimized for high energy resolution, and exhibit image aberrations when operated in high transmission mode at medium energy resolution. Here, a new approach is presented for bandpass-filtered imaging in real or reciprocal space using an electrostatic dodecapole with an asymmetric electrode array. In addition to energy-dispersive beam deflection, this multipole allows aberration correction up to the third order. Here, its use is described as a bandpass prefilter in a time-of-flight momentum microscope at the hard X-ray beamline P22 of PETRA III. The entire instrument is housed in a straight vacuum tube because the deflection angle is only 4° and the beam displacement in the filter is only ∼8 mm. The multipole is framed by transfer lenses in the entrance and exit branches. Two sets of 16 different-sized entrance and exit apertures on piezomotor-driven mounts allow selection of the desired bandpass. For pass energies between 100 and 1400 eV and slit widths between 0.5 and 4 mm, the transmitted kinetic energy intervals are between 10 eV and a few hundred electronvolts (full width at half-maximum). The filter eliminates all higher or lower energy signals outside the selected bandpass, significantly improving the signal-to-background ratio in the time-of-flight analyzer.

The differentially pumped rare-gas filter at the end of the VUV beamline of the Swiss Light Source has been adapted to house a windowless absorption cell for gases. Absorption spectra can be recorded from 7 eV to up to 21 eV photon energies routinely, as shown by a new water and nitrous oxide absorption spectrum. By and large, the spectra agree with previously published ones both in terms of resonance energies and absorption cross sections, but that of N2O exhibits a small shift in the { ilde{f D}} band and tentative fine structures that have not yet been fully described. This setup will facilitate the measurement of absorption spectra in the VUV above the absorption edge of LiF and MgF2 windows. It will also allow us to carry out condensed-phase measurements on thin liquid sheets and solid films. Further development options are discussed, including the recording of temperature-dependent absorption spectra, a stationary gas cell for calibration measurements, and the improvement of the photon energy resolution.

Macromolecular crystallography contributes significantly to understanding diseases and, more importantly, how to treat them by providing atomic resolution 3D structures of proteins. This is achieved by collecting X-ray diffraction images of protein crystals from important biological pathways. Spotfinders are used to detect the presence of crystals with usable data, and the spots from such crystals are the primary data used to solve the relevant structures. Having fast and accurate spot finding is essential, but recent advances in synchrotron beamlines used to generate X-ray diffraction images have brought us to the limits of what the best existing spotfinders can do. This bottleneck must be removed so spotfinder software can keep pace with the X-ray beamline hardware improvements and be able to see the weak or diffuse spots required to solve the most challenging problems encountered when working with diffraction images. In this paper, we first present Bragg Spot Detection (BSD), a large benchmark Bragg spot image dataset that contains 304 images with more than 66 000 spots. We then discuss the open source extensible U-Net-based spotfinder Bragg Spot Finder (BSF), with image pre-processing, a U-Net segmentation backbone, and post-processing that includes artifact removal and watershed segmentation. Finally, we perform experiments on the BSD benchmark and obtain results that are (in terms of accuracy) comparable to or better than those obtained with two popular spotfinder software packages (Dozor and DIALS), demonstrating that this is an appropriate framework to support future extensions and improvements.

The Australian Synchrotron Imaging and Medical Beamline (IMBL) uses a superconducting multipole wiggler (SCMPW) source, dual crystal Laue monochromator and 135 m propagation distance to enable imaging and computed tomography (CT) studies of large samples with mono-energetic radiation. This study aimed to quantify two methods for CT dose reduction: wiggler source operation at reduced magnetic field strength, and beam modulation with spatial filters placed upstream from the sample. Transmission measurements with copper were used to indirectly quantify the influence of third harmonic radiation. Operation at lower wiggler magnetic field strength reduces dose rates by an order of magnitude, and suppresses the influence of harmonic radiation, which is of significance near 30 keV. Beam shaping filters modulate the incident beam profile for near constant transmitted signal, and offer protection to radio-sensitive surface organs: the eye lens, thyroid and female breast. Their effect is to reduce the peripheral dose and the dose to the scanned volume by about 10% for biological samples of 35–50 mm diameter and by 20–30% for samples of up to 160 mm diameter. CT dosimetry results are presented as in-air measurements that are specific to the IMBL, and as ratios to in-air measurements that may be applied to other beamlines. As CT dose calculators for small animals are yet to be developed, results presented here and in a previous study may be used to estimate absorbed dose to organs near the surface and the isocentre.

EPA should conduct an ecological risk assessment of ultraviolet filters used in sunscreens, given the evidence that aquatic ecosystems in the U.S. and possibly endangered species are exposed to these UV filters, and given the importance of these ingredients in skin cancer prevention.

VITO Fryfilter, Inc. recently released an intensive cleaner individually packed as tabs, for an effective cleaning of commercial deep fryers: the VITO tab.

VITO AG has announced the launch of a new product to fulfill the rising need for high-quality and easy to use filtration systems for deep fryers: the VITO VL oil filter system.

Installing the magnetic filter will increase energy efficiency, significantly prolong boiler life, protect pipework and considerably reduce maintenance calls, the company notes.

UFI provides filtration solutions to domestic and international customers servicing food, beverage, potable and wastewater applications.

NSF International Standard 53 is internationally recognized as the benchmark for water filtration systems’ effectiveness in reducing specific health-related contaminants. 

The Senior Dating Site Adds Filter Feature to Help Seniors Find Love with Greater Ease

Shower Dog: World's First Shower Filter Exclusively for Dogs

Government research conducted on July 5, 2023, and reported by the Orlando Sentinel reveals a concerning problem: nearly 50% of tap water in the United States contains potentially harmful 'forever chemicals.'

SoBellas offers a wide range of filters, with a comprehensive selection of air filters to cater to various HVAC systems, ensuring customers find the perfect fit for their specific needs.

Anna Riha, Nikolas Siccha, Antti Oulasvirta, and Aki Vehtari write: When building statistical models for Bayesian data analysis tasks, required and optional iterative adjustments and different modelling choices can give rise to numerous candidate models. In particular, checks and evaluations … Continue reading →

In addition to our Leveler, Denoiser, and Adaptive 'Hi-Pass' Filter, we now release the missing equalization feature with the new Auphonic AutoEQ.
The AutoEQ automatically analyzes and optimizes the frequency spectrum of a voice recording, to remove sibilance (De-esser) and to create a clear, warm, and pleasant sound - listen to the audio examples below to get an idea about what it does.

Screenshot of manually adjusted example settings for the equalizer plug-in 'Pro-Q3' by fabfilter.

What is Equalization and why is it difficult?

Equalization (EQ) in audio recording and reproduction is the process of adjusting the volume of different frequency bands within a signal.
The following vocal EQ cheat sheet, published by Producer Hive, will give you a small impression, of what can be influenced by equalizing:

Vocal EQ Cheat Sheet by Producer Hive.

On the other hand, it is very easy to ruin a good voice recording with manual heavy-handed equalization, resulting in voices, that sound very sharp or muddy or even like the speaker had a blocked nose.
Besides the skill and experience of an audio engineer, manual adjustments of frequencies also require a very good and linear studio playback device. For example, performing manual equalization with strongly bass-heavy speakers would most likely lead to a very sharp, unpleasant listening experience using treble-heavy headphones.

For singletrack productions with more than one speaker, equalizing is also a very complex and time-consuming process, as every voice has its unique frequency spectrum and needs its own equalization. One could separate speakers with cuts or create a track envelope to fade from one speaker to another, however, any solution is a very tedious job, if you do it by hand.

That is where the Auphonic AutoEQ comes in! All those steps are now available in just one click!

How does the Auphonic AutoEQ work?

The Auphonic Web Service analyzes your audio content and classifies the audio file into small and meaningful segments like music, silence, different speakers, etc. to process every single segment with the best matching algorithms.
All our features like the Adaptive Leveler, Dynamic Denoising, Adaptive 'Hi-Pass' Filtering, and now the new AutoEQ filter option are built on top of this basic processing.

Using Auphonic AutoEQ, spectral EQ profiles are created for each speaker separately and permanently changing over time. The aim of those time-dependent EQ profiles is to create a constant, pleasant sound in the output file even if there are slightly changing voices in the record, for example, due to modified speaker-microphone positions.

Audio Examples

Here are two short audio examples, which demonstrate some features of our AutoEQ.
We recommend listening with headphones so you can hear all the details.

Example 1. Female Speaker with Background Music

In the following example (BCB: The Voices of Bainbridge Island) of a female narrator speaking while background music is playing, you can easily recognize quite sharp 'sss' sounds in the female voice. This sharpness in the female voice is removed by the so-called De-essing feature of the Auphonic AutoEQ, while the background music is not changed.

Example 2. Dialog of Male and Female Speakers

The next example (BCB: The Voices of Bainbridge Island) shows how the AutoEQ optimizes a singletrack record containing two speakers with different voice characteristics. Our AutoEQ algorithms analyze each voice separately and calculate the matching frequency adjustments to optimize the voice of every single speaker.

AutoEQ Beta Integration in the Auphonic Web Service

To use the Auphonic AutoEQ, you just have to create a production or preset as you are used to, toggle “Advanced Parameters” on the top right in the section “Audio Algorithms” and select “Voice AutoEQ” within “Filtering”:

For a first test period, the AutoEQ will only be available for Beta and paying users, to incorporate your feedback and finalize an optimized version.
If you are a free user but want to try Auphonic AutoEQ: please just ask for access!

Practical Tips

For best results using Auphonic AutoEQ, however, it is still necessary, that your audio content is of sufficiently good quality, as no equalizer can make up frequencies that are not there in the first place. Audio files with low bitrates often lack important frequencies, that cannot be recovered by equalizing. AutoEQ is just a feature to boost or cut individual frequency bands, not a bandwidth extension. For more information about required audio quality, see the former blog post: Audio File Formats and Bitrates for Podcasts.

Another important topic is the definition of the 'best result'. Equalizing is a very subjective task, that differs a lot depending on every personal opinion. So Auphonic AutoEQ is set up to follow quite conservative rules of equalizing and rather apply subtle tweaks and remove obvious problems, than support personal preferences. This also means, your record will experience no significant changes from Auphonic AutoEQ if it sounds reasonably OK or pretty good already.

Conclusion

Auphonic audio post production algorithms keep getting better and better in leaps and bounds lately, offering you new Beta Features: Beta Auphonic Denoiser, Beta Auphonic Speech Recognition, and Beta Auphonic AutoEQ.
Right now we are fine-tuning all our current Beta Features with high intensity to release a new upgraded version of our Auphonic Web Service as soon as possible.
Please watch this channel for further updates – soon to come.

If you have any feedback for us or want to become a Beta user, you are very welcome to comment directly in our production interface or to contact us via email!

Have you ever seen the realistic concept art of games with many buildings and tall towers? Or the pictures of busy markets with giant billboards and advertisements? Or the images with street perspectives, including Photoshopped texts and digitally painted graffiti? These images grab our attention and appeal to our eyes. For a designer, artist, or...

The post How to Use the Vanishing Point Filter in Adobe Photoshop appeared first on Bittbox.

Announcing the release of PhotoNetCast #88, the ultimate filter episode.

This excellent infographic created by lovethegarden.com is based on The NASA Clean Air Study of 1989. While researching ways to clean air in space stations, it was determined that these air-filtering houseplants significantly eliminate benzene, formaldehyde and trichloroethylene from the … Continue reading →