Noise Impact and Improvement on Indoors Acoustic Comfort for the Building Adjacent to Heavy Traffic Road

A good acoustic environment is absolutely essential to maintaining a high level satisfaction and moral health among residents. Noise and other boresome sounds come from both in- door and outdoor sources. For the residential buildings adjacent to heavy traffic roads, outdoors traffic noise is the main source that affects indoor acoustic quality and health. Ventilation and outdoor noise prevention become a pair of contradictions for the residents in China nowadays for those buildings adjacent to heavy traffic roads. It is investigated that traffic noise emission is mainly con- stituted by the motors of trucks, buses and motorcycles as well as brake. In this paper, two methods of traffic noise reduction on the indoor sound environment and comfort are carried out to study and compare the residential buildings adjacent to heavy traffic roadway in a city. One is to install noise barriers on the two sides of the roadway, which consist of sound-proof glass and plas- tic materials. The effect of sound-insulation of this method is heavily dependent on the relative distance between the noise bar- rier and indoors. A reduction of sound with an average pressure level of 2–15dB is achieved on the places behind and under the noise barrier. However, for the equivalent of noise barrier height, the noise reduction effect is little. As for the places of higher than the noise barrier, the traffic noise will be even strengthened by 3–7dB. Noise increment can be seen at the points of distance farther than 15m and height more than noise barrier; the noise reduction effect is not satisfactory or even worsened. In addition, not every location is appropriate to install the noise barrier along the heavy traffic roads. The other method of noise reduction for the buildings adjacent to heavy traffic is to install the airproof and soundproof windows, which is the conversion from natural venti- lation to mechanical ventilation. A reduction of sound with an average pressure level of 5dB to 17dB can be achieved compared with common glass windows, if adopting sound proof glass win- dows. These two methods are helpful to isolate high frequency noise but not for low frequency noise. For those frequency noises, installing thick and cotton curtain and porous carpet can only decrease 2.4–4.5dB, which hardly contributes to indoor sound comfort, so further study is demanded to cut down traffic noise, especially to cut down the low frequency noise.

面向自组织人群的建筑安全评估算法

为了解决室内建筑节点不合理与人群逃生路线选择困难的问题,提出面向自组织人群的建筑安全评估算法.基于IndoorGML建立室内空间的水平平面结构和垂直层级结构,形成拓扑空间中的节点关联图,以表示多层建筑物的空间结构.通过对单元空间的风险、通行率的定义,设计基于A^(*)算法的安全疏散路线选择算法.通过分析逃生路线的选择人数、单条逃生路线上节点的最高风险、单条逃生路线的平均逃生时间,找出室内建筑不合理节点并提出优化方案.构建模拟场景进行仿真实验,对比不同路线选择方法的结果,分析并验证所提评估算法的有效性.结果表明,所提出的方法能够找出建筑拥堵的主要因素及其对疏散的影响程度,有助于在建筑设计阶段开展科学的建筑安全评估.

Free-walking:Pedestrian inertial navigation based on dual foot-mounted IMU

The inertial navigation system(INS),which is frequently used in emergency rescue operations and other situations,has the benefits of not relying on infrastructure,high positioning frequency,and strong real-time performance.However,the intricate and unpredictable pedestrian motion patterns lead the INS localization error to significantly diverge with time.This paper aims to enhance the accuracy of zero-velocity interval(ZVI)detection and reduce the heading and altitude drift of foot-mounted INS via deep learning and equation constraint of dual feet.Aiming at the observational noise problem of low-cost inertial sensors,we utilize a denoising autoencoder to automatically eliminate the inherent noise.Aiming at the problem that inaccurate detection of the ZVI detection results in obvious displacement error,we propose a sample-level ZVI detection algorithm based on the U-Net neural network,which effectively solves the problem of mislabeling caused by sliding windows.Aiming at the problem that Zero-Velocity Update(ZUPT)cannot suppress heading and altitude error,we propose a bipedal INS method based on the equation constraint and ellipsoid constraint,which uses foot-to-foot distance as a new observation to correct heading and altitude error.We conduct extensive and well-designed experiments to evaluate the performance of the proposed method.The experimental results indicate that the position error of our proposed method did not exceed 0.83% of the total traveled distance.

SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach:Multistory Round Building Scenario over LoRa Network

In situations when the precise position of a machine is unknown,localization becomes crucial.This research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based technique.In order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based algorithm.Received signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout building.The noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI measurement.This study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference point.The findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.

Scenario Modeling-Aided AP Placement Optimization Method for Indoor Localization and Network Access

Owing to the ubiquity of wireless networks and the popularity of WiFi infrastructures,received signal strength(RSS)-based indoor localization systems have received much attention.The placement of access points(APs)significantly influences localization accuracy and network access.However,the indoor scenario and network access are not fully considered in previous AP placement optimization methods.This study proposes a practical scenario modelingaided AP placement optimization method for improving localization accuracy and network access.In order to reduce the gap between simulation-based and field measurement-based AP placement optimization methods,we introduce an indoor scenario modeling and Gaussian process-based RSS prediction method.After that,the localization and network access metrics are implemented in the multiple objective particle swarm optimization(MOPSO)solution,Pareto front criterion and virtual repulsion force are applied to determine the optimal AP placement.Finally,field experiments demonstrate the effectiveness of the proposed indoor scenario modeling method and RSS prediction model.A thorough comparison confirms the localization and network access improvement attributed to the proposed anchor placement method.

Improved PSO-Extreme Learning Machine Algorithm for Indoor Localization

Wi Fi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related features.The basic assumption of fingerprinting localization is that the received signal strength indication(RSSI)distance is accord with the location distance.Therefore,how to efficiently match the current RSSI of the user with the RSSI in the fingerprint database is the key to achieve high-accuracy localization.In this paper,a particle swarm optimization-extreme learning machine(PSO-ELM)algorithm is proposed on the basis of the original fingerprinting localization.Firstly,we collect the RSSI of the experimental area to construct the fingerprint database,and the ELM algorithm is applied to the online stages to determine the corresponding relation between the location of the terminal and the RSSI it receives.Secondly,PSO algorithm is used to improve the bias and weight of ELM neural network,and the global optimal results are obtained.Finally,extensive simulation results are presented.It is shown that the proposed algorithm can effectively reduce mean error of localization and improve positioning accuracy when compared with K-Nearest Neighbor(KNN),Kmeans and Back-propagation(BP)algorithms.

A UWB/IMU-Assisted Fingerprinting Localization Framework with Low Human Efforts

With the rapid development of smart phone,the location-based services(LBS)have received great attention in the past decades.Owing to the widespread use of WiFi and Bluetooth devices,Received Signal Strength Indication(RSSI)fingerprintbased localization method has obtained much development in both academia and industries.In this work,we introduce an efficient way to reduce the labor-intensive site survey process,which uses an UWB/IMU-assisted fingerprint construction(UAFC)and localization framework based on the principle of Automatic radio map generation scheme(ARMGS)is proposed to replace the traditional manual measurement.To be specific,UWB devices are employed to estimate the coordinates when the collector is moved in a reference point(RP).An anchor self-localization method is investigated to further reduce manual measurement work in a wide and complex environment,which is also a grueling,time-consuming process that is lead to artificial errors.Moreover,the measurements of IMU are incorporated into the UWB localization algorithm and improve the label accuracy in fingerprint.In addition,the weighted k-nearest neighbor(WKNN)algorithm is applied to online localization phase.Finally,filed experiments are carried out and the results confirm the effectiveness of the proposed approach.

Modulating perovskite crystallization and band alignment using coplanar molecules for high-performance indoor photovoltaics

The proper bandgap and exceptional photostability enable CsPbI_(3) as a potential candidate for indoor photovoltaics(IPVs),but indoor power conversion efficiency(PCE) is impeded by serious nonradiative recombination stemming from challenges in incomplete DMAPbI_(3) conversion and lattice structure distortion.Here,the coplanar symmetric structu re of hexyl sulfide(HS) is employed to functionalize the CsPbI_(3) layer for fabricating highly efficient IPVs.The hydrogen bond between HS and DMAI promotes the conversion of DMAPbI_(3) to CsPbI_(3),while the copianar symmetric structure enhances crystalline order.Simultaneously,surface sulfidation during HS-induced growth results in the in situ formation of PbS,spontaneously creating a CsPbI_(3) N-P homojunction to enhance band alignment and carrier mobility.As a result,the CsPbI_(3)&HS devices achieve an impressive indoor PCE of 39.90%(P_(in):334.6 μW cm^(-2),P_(out):133.5 μW cm^(-2)) under LED@2968 K,1062 lux,and maintain over 90% initial PCE for 800 h at ^(3)0% air ambient humidity.

Survey of Indoor Localization Based on Deep Learning

This study comprehensively examines the current state of deep learning (DL) usage in indoor positioning.It emphasizes the significance and efficiency of convolutional neural networks (CNNs) and recurrent neuralnetworks (RNNs). Unlike prior studies focused on single sensor modalities like Wi-Fi or Bluetooth, this researchexplores the integration of multiple sensor modalities (e.g.,Wi-Fi, Bluetooth, Ultra-Wideband, ZigBee) to expandindoor localization methods, particularly in obstructed environments. It addresses the challenge of precise objectlocalization, introducing a novel hybrid DL approach using received signal information (RSI), Received SignalStrength (RSS), and Channel State Information (CSI) data to enhance accuracy and stability. Moreover, thestudy introduces a device-free indoor localization algorithm, offering a significant advancement with potentialobject or individual tracking applications. It recognizes the increasing importance of indoor positioning forlocation-based services. It anticipates future developments while acknowledging challenges such as multipathinterference, noise, data standardization, and scarcity of labeled data. This research contributes significantly toindoor localization technology, offering adaptability, device independence, and multifaceted DL-based solutionsfor real-world challenges and future advancements. Thus, the proposed work addresses challenges in objectlocalization precision and introduces a novel hybrid deep learning approach, contributing to advancing locationcentricservices.While deep learning-based indoor localization techniques have improved accuracy, challenges likedata noise, standardization, and availability of training data persist. However, ongoing developments are expectedto enhance indoor positioning systems to meet real-world demands.

Fine-grained grid computing model for Wi-Fi indoor localization in complex environments

The fingerprinting-based approach using the wireless local area network(WLAN)is widely used for indoor localization.However,the construction of the fingerprint database is quite time-consuming.Especially when the position of the access point(AP)or wall changes,updating the fingerprint database in real-time is difficult.An appropriate indoor localization approach,which has a low implementation cost,excellent real-time performance,and high localization accuracy and fully considers complex indoor environment factors,is preferred in location-based services(LBSs)applications.In this paper,we proposed a fine-grained grid computing(FGGC)model to achieve decimeter-level localization accuracy.Reference points(RPs)are generated in the grid by the FGGC model.Then,the received signal strength(RSS)values at each RP are calculated with the attenuation factors,such as the frequency band,three-dimensional propagation distance,and walls in complex environments.As a result,the fingerprint database can be established automatically without manual measurement,and the efficiency and cost that the FGGC model takes for the fingerprint database are superior to previous methods.The proposed indoor localization approach,which estimates the position step by step from the approximate grid location to the fine-grained location,can achieve higher real-time performance and localization accuracy simultaneously.The mean error of the proposed model is 0.36 m,far lower than that of previous approaches.Thus,the proposed model is feasible to improve the efficiency and accuracy of Wi-Fi indoor localization.It also shows high-accuracy performance with a fast running speed even under a large-size grid.The results indicate that the proposed method can also be suitable for precise marketing,indoor navigation,and emergency rescue.

Passive IoT Localization Technology Based on SD-PDOA in NLOS and Multi-Path Environments

Addressing the challenges of passive Radio Frequency Identification(RFID)indoor localization technology in Non-Line-of-Sight(NLoS)and multipath environments,this paper presents an innovative approach by introducing a combined technology integrating an improved Kalman Filter with Space Domain Phase Difference of Arrival(SD-PDOA)and Received Signal Strength Indicator(RSSI).This methodology utilizes the distinct channel characteristics in multipath and NLoS contexts to effectively filter out interference and accurately extract localization information,thereby facilitating high precision and stability in passive RFID localization.The efficacy of this approach is demonstrated through detailed simulations and empirical tests conducted on a custom-built experimental platform consisting of passive RFID tags and an R420 reader.The findings are significant:in NLoS conditions,the four-antenna localization system achieved a notable localization accuracy of 0.25 m at a distance of 5 m.In complex multipath environments,this system achieved a localization accuracy of approximately 0.5 m at a distance of 5 m.When compared to conventional passive localization methods,our proposed solution exhibits a substantial improvement in indoor localization accuracy under NLoS and multipath conditions.This research provides a robust and effective technical solution for high-precision passive indoor localization in the Internet of Things(IoT)system,marking a significant advancement in the field.

Impact of the Inlet Flow Angle and Outlet Placement on the Indoor Air Quality

This study aims to optimize the influence of the inlet inclination angle on the Indoor Air Quality(IAQ),heat,and temperature distribution in mixed convection within a two-dimensional square cavityfilled with an air-CO_(2)mixture.The air-CO_(2)mixture enters the cavity through two inlet openings positioned at the top wall,which is set at the ambient temperature(TC).Three values of the Reynolds numbers,ranging from 1000 to 2000,are considered,while the Prandtl number is kept constant(Pr=0.71).The temperature distribution and streamlines are shown for Rayleigh number(Ra)equal to 104,three inlet inclination anglesϕ(0,π/6 andπ/4)and three CO_(2)concentrations values(1500,2500,3500 ppm)applied at both hot vertical walls(maintained at a constant temperature TH).Afinite volume method is used under the assumption of two-dimensional laminarflow to solve the NavierStokes and energy equations.The results indicate that inlet inclination angle has an impact on the indoor air quality(IAQ),which,in turn,affects the heat transfer distribution and thermal comfort within the cavity.

Research and development on mechanism of removal of indoor volatile organic compounds by plants

Background,aim,and scope Owing to the rapid development of modernisation and urbanisation,living standards have gradually improved.However,the widespread use of high-energy-consuming indoor appliances and furniture has made indoor environments a primary environmental problem affecting human health.Sick building syndrome(SBS)and building-related illness(BRI)have occurred,and indoor air conditions have been extensively studied.Common indoor pollutants include CO,CO_(2),volatile organic compounds(VOCs)(such as the formaldehyde and benzene series),NOx(NO and NO_(2)),and polycyclic aromatic hydrocarbons(PAHs).VOCs have replaced SO_(2)as the“The Fourteenth Five-Year Plan”urban air quality assessment new indicators.Indoor VOCs can cause diseases such as cataract,asthma,and lung cancer.To protect human health,researchers have proposed several indoor air purification technologies,including adsorption,filtration,electrostatic dust removal,ozonation,and plant purification.However,each technology has drawbacks,such as high operating costs,high energy consumption,and the generation of secondary waste or toxic substances.Plant degradation of VOCs as a bioremediation technology has the characteristics of low cost,high efficiency,and sustainability,thereby becoming a potential green solution for improving indoor air quality.This study introduces the research status and mechanism of plant removal of indoor VOCs and provides an experimental basis and scientific guidance for analysing the mechanism of plant degradation of pollutants.Materials and methods This study reviews studies on the harm caused by indoor pollutants to human health and related sources,mainly investigating the degradation of indoor formaldehyde,BTEX(benzene,toluene,ethylbenzene,and xylene)plant mechanisms,and research results.Results Plants can remove VOCs via stomatal and non-stomatal adsorption,interfoliar microbial,rhizosphere microbial,and growth media.Benzene,toluene,and xylene(BTX)are adsorbed by pores,hydroxylated into fumaric acid,and then removed into CO_(2) and H_(2)O by TCA.Formaldehyde enters plant leaves through the stomata and epidermal waxy substances and is adsorbed.After the two steps of enzymatic oxidation,formic acid and CO_(2) are generated.Finally,it enters the Calvin cycle and removes glucose and other nontoxic compounds.Discussion The non-stomatal degradation of VOCs can be divided into adsorption by cuticular wax and active adsorption by plant surface microorganisms.The leaf epidermal waxy matter content and the lipid composition of the epidermal membrane covering the plant surface play important roles in the non-stomatal adsorption of indoor air pollutants.The leaf margin of a plant is an ecological environment containing various microbial communities.The endophytic and inoculated microbiota in plant buds and leaves can remove VOCs(formaldehyde and BTEX).Formaldehyde can be directly absorbed by plant leaves and converted into organic acids,sugars,CO_(2) and H_(2)O by microbes.Bioremediation of indoor VOCs is usually inefficient,leading to plant toxicity or residual chemical substance volatilisation through leaves,followed by secondary pollution.Therefore,plants must be inoculated with microorganisms to improve the efficiency of plant degradation of VOCs.However,the effectiveness of interfoliar microbial removal remains largely unknown and several microorganisms are not culturable.Therefore,methods for collecting,identifying,and culturing microorganisms must be developed.As the leaf space is a relatively unstable environment,the degradation of VOCs by rhizosphere microorganisms is equally important,and formaldehyde is absorbed more by rhizosphere microorganisms at night.The inoculation of bacteria into the rhizosphere improves the efficiency of plants in degrading VOCs.However,most of these studies were conducted in simulation chambers.To ensure the authenticity of these conclusions,the ability of plants to remove indoor air pollutants must be further verified in real situations.Conclusions Plant purification is an economical,environment-friendly,and sustainable remediation technology.This review summarises the mechanisms of VOC plant degradation and presents its limitations.Simultaneously,it briefly puts forward a plant selection scheme according to different temperatures,light,and specific VOCs that can be absorbed to choose the appropriate plant species.However,some studies have denied the purification effect of plants and proposed that numerous plants are required to achieve indoor ventilation effects.Therefore,determining the ability of plants to remove indoor VOCs requires a combination of realistic and simulated scenarios.Recommendations and perspectives Plants and related microorganisms play an important role in improving indoor air quality,therefore,the effect of plants and the related microorganisms on improving indoor air quality must be studied further and the effect of plants on indoor VOCs will be the focus of future research.

An organic visible-photocatalytic-adsorbence mechanism to high-efficient removal of heavy metal antimony ions

Purification of emerging heavy metal antimony contaminated water based on advanced ingenious strategies.An activated modified coconut shell charcoal(CSC)was synthesized and evaluated as a substrate-supported loaded organic photovoltaic material,PM6:PYIT:PM6-b-PYIT,to prepare a surprisingly highly efficient,stable,environmentally friendly,and recyclable organic photocatalyst(CSC–N–P.P.P),which showed excellent effects on the simultaneous removal of Sb(Ⅲ)and Sb(Ⅴ).The removal efficiency of CSC-N-P.P.P on Sb(Ⅲ)and Sb(Ⅴ)reached an amazing 99.9%in quite a short duration of 15 min.At the same time,under ppb level and indoor visible light(~1 W m^(2)),it can be treated to meet the drinking water standards set by the European Union and the U.S.National Environmental Protection Agency in 5 min,and even after 25 cycles of recycling,the efficiency is still maintained at about 80%,in addition to the removal of As(Ⅲ),Cd(Ⅱ),Cr(Ⅵ),and Pb(Ⅱ)can also be realized.The catalyst not only solves the problems of low reuse rate,difficult structure adjustment and high energy consumption of traditional photocatalysts but also has strong applicability and practical significance.The pioneering approach provides a much-needed solution strategy for removing highly toxic heavy metal antimony pollution from the environment.

Suppressing Leakage Currents and Improving Performance of Indoor Organic Photovoltaic Devices

Organic photovoltaic(OPV) devices hold great promise for indoor light harvesting,offering a theoretical upper limit of power conversion efficiency that surpasses that of other photovoltaic technologies.However,the presence of high leakage currents in OPV devices commonly constrains their effective performance under indoor conditions.In this study,we identified that the origin of the high leakage currents in OPV devices lay in pinhole defects present within the active layer(AL).By integrating an automated spin-coating strategy with sequential deposition processes,we achieved the compactness of the AL and minimized the occurrence of pinhole defects therein.Experimental findings demonstrated that with an increase in the number of deposition cycles,the density of pinhole defects in the AL underwent a marked reduction.Consequently,the leakage current experienced a substantial decrease by several orders of magnitude which achieved through well-calibrated AL deposition procedures.This enabled a twofold enhancement in the power conversion efficiency(PCE) of the OPV devices under conditions of indoor illumination.

The Thermal Comfort and Hygrothermal Performance of the First Certified Passive House in Estonia

This study presents the thermal comfort and hygrothermal performance of building envelope of the first certified passive single-family detached house in Estonia.Temperature and humidity conditions were measured from different rooms and building envelopes.This article presents analysis of measurement results during the first year after construction.Results showed high room temperature,achieved mainly due to large windows with southern exposure and the small heat loss of the building envelope.High indoor temperature decreased the indoor RH(relative humidity)to quite low levels.Even the RH was low,the moisture excess was high indicating that the design of PH(passive houses)indoor humidity loads cannot be decreased.Humidity in the externally insulated cross-laminated timber panels was observed to be high,caused by drying out of the constructional moisture and the high diffusion resistance of the wood fibre sheathing board.That caused water vapour condensation and risk for mould growth.In conclusion,while planning buildings with high-energy efficiency,more focused attention should be paid to the performance of the building service systems and moisture safety already in the preliminary stages of design.

A Comparison of Airborne Formaldehyde Field Measurements Collected in an Anatomic Pathology Laboratory

Environmental monitoring of airborne formaldehyde (FA) using sensitive methodologies is fundamental to prevent health risks. The objective of this study was to compare three different FA monitoring methods during the daily activities of an anatomic pathology laboratory. Daily eight-hour measurements deriving from Radiello® passive diffusive samplers (PDS), NEMo XT continuous optical sensor (COS), and multi-gas 1512 photoacoustic monitor (MPM) were simultaneously compared over a period of 14 working days. Given the different daily distributions of the measurements performed by the three devices, all measurements were time-aligned for comparison purposes. The 95% limit of agreement (LOA) method was applied to estimate the degree of concordance of each device with respect to the others. Formaldehyde arithmetic mean measured using PDS was 32.6 ± 10.4 ppb (range: 19.8 - 62.7). The simultaneous measures performed by COS and MPM were respectively 42.4 ± 44.8 ppb (range: 7.0 - 175.0) and 189.0 ± 163.7 ppb (range: 40.0 - 2895.4). The MPM geometric mean (171.3 ppb) was approximately five times higher than those derived from COS (32.3 ppb) and PDS (31.4 ppb). The results of the LOA method applied to log-transformed FA data showed the same systematic discrepancies between MPM and the other two devices. A good agreement between PDS and COS could lead to a tailored approach according to the individual specificity of these techniques. This tool may be useful for accurately assessing the risk of FA exposure among healthcare workers. However, the limited specificity of the MPM does not support its use as a monitoring method for FA in the workplace.

Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

A Natural Catalytic Converter® for Continuously Inactivating Air and Surface Pathogens with More Effect than Ventilation and Filtration

Study Objective: The purpose of the study is to present independent laboratory testing for a novel technology in air and on surfaces. Since 2020, public health goals have focused on improving indoor air quality. This includes protection from airborne pathogens, such as tuberculosis, RSV, SARS-CoV-2, common cold or influenza viruses, measles, and others. Engineering controls are highly effective at reducing hazardous pathogens found in indoor air and from recontamination of surfaces. This occurs from a continuous cycle of settling of small, sustained airborne pathogens, which may become dehumidified, becoming airborne again, carried by room air currents around indoor spaces, then repeating the cycle. Methods: The novel technology utilizes a catalytic process to produce safe levels of hydrogen peroxide gas that are effective in reducing pathogens in the air and on surfaces. Air testing was performed with the MS2 bacteriophage, the test organism for ASHRAE standard 241, and methicillin-Resistant Staphylococcus aureus (MRSA). Surface testing was performed with SARS-COV-2 (Coronavirus COVID-19) and H1N1 (Influenza). Typical ventilation and filtration does not effectively remove disbursed pathogens from the entire facility, due to inconsistent air circulation and surface deposits of pathogens. Results: MS2 was reduced by 99.9%;MRSA was reduced by 99.9%;SARS-CoV-2 was reduced by 99.9%;H1N1 was reduced by 99.9%. Conclusion: This novel catalytic converter reduces a variety of pathogens in the air (99%) and on surfaces (99%), by actively disinfecting with the introduction of gaseous hydrogen peroxide. This active disinfection provides a strong solution for protecting the entire facility and its occupants.

Thermal Performance Analysis of Plaster Reinforced with Raffia Vinifera Particles for Use as Insulating Materials in Building

The present study focuses on the formulation of new composite consisting of plaster and raffia vinifera particle (RVP) with the purpose to reducing energy consumption. The aim of this study is to test this new compound as an insulating eco-material in building in a tropical climate. The composites samples were developed by mixing plaster with raffia vinifera particles (RVP) using three different sizes (1.6 mm, 2.5 mm and 4 mm). The effects of four different RVP incorporations rates (i.e., 0wt%, 5wt%;10wt%;15wt%) on physical, thermal, mechanicals properties of the composites were investigated. In addition, the use of the raffia vinifera particles and plaster based composite material as building envelopes thermal insulation material is studied by the habitable cell thermal behavior instrumentation. The results indicate that the incorporation of raffia vinifera particle leads to improve the new composite physical, mechanical and thermal properties. And the parametric analysis reveals that the sampling rate and the size of raffia vinifera particles are the most decisive factor to impact these properties, and to decreases in the thermal conductivity which leads to an improvement to the thermal resistance and energy savings. The best improvement of plaster composite was obtained at the raffia vinifera particles size between 2.5 and 4.0 mm loading of 5wt% (C95P5R) with a good ratio of thermo-physical-mechanical properties. Additionally, the habitable cell experimental thermal behavior, with the new raffia vinifera particles and plaster-based composite as thermal insulating material for building walls, gives an average damping of 4°C and 5.8°C in the insulated house interior environment respectively for cold and hot cases compared to the outside environment and the uninsulated house interior environment. The current study highlights that this mixture gives the new composite thermal insulation properties applicable in the eco-construction of habitats in tropical environments.