Impact of Indoor Residual-Sprayed Deltamethrin on Different Surfaces in a Malaria Endemic Area in Balai Ringin, Sarawak

Malaria control programme utilizing indoor residual spraying of chemical insecticide is only effective if a high coverage of targeted area is achieved. The effectiveness of the residual spraying, on the other hand, relies on the efficacy and residual activity of the insecticides applied, which to a certain extent are influenced by the nature of the sprayed surfaces. The bioefficacy of indoor residual-sprayed deltamethrin wettable granule (WG) formulation for the control of malaria was compared with the current dose of deltamethrin wettable powder (WP) in malaria endemic areas in Balai Ringin, Sarawak. Doses of 20 mg/m2 WP (control), 20 mg/m2 WG, 30 mg/m2 WG and 40 mg/m2 WG were sprayed separately on different surfaces namely, wooden, rough-bamboo, smooth-bamboo and brick surfaces. Residual activity of WP and WG formulations was tested against lab-bred Anopheles maculatus using WHO standard procedure. Deltamethrin at 30 mg/m2 WG exhibited the highest sustainable level of effectiveness against An. maculatus (An. maculatus mortality was between 95% - 100%) up to week 60 post-spraying when sprayed on smooth- bamboo surface. These results indicated that 30 mg/m2 WG could be an ideal concentration for controlling malaria vector effectively up to 15 months of which long-lasting residual spraying was envisaged. The usual two spraying cycles per year with 20 mg/m2 deltamethrin WP could be replaced with 30 mg/m2 deltamethrin WG since the long residual activity was achieved by employing a single spraying only.

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.

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.

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.

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.

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.

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.

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.

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.

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.

WiFi Indoor Positioning and Tracking Algorithm Based on Compressive Sensing and Sage-Husa Adaptive Kalman Filter

Aiming at the problem that the positioning accuracy of WiFi indoor positioning technology based on location fingerprint has not reached the requirements of practical application, a WiFi indoor positioning and tracking algorithm combining adaptive affine propagation (AAPC), compressed sensing (CS) and Kalman filter is proposed. In the off-line phase, AAPC algorithm is used to generate clustering fingerprints with optimal clustering effect performance;In the online phase, CS and nearest neighbor algorithm are used for position estimation;Finally, the Kalman filter and physical constraints are combined to perform positioning and tracking. By collecting a large number of real experimental data, it is proved that the developed algorithm has higher positioning accuracy and more accurate trajectory tracking effect.

A Visual Indoor Localization Method Based on Efficient Image Retrieval

The task of indoor visual localization, utilizing camera visual information for user pose calculation, was a core component of Augmented Reality (AR) and Simultaneous Localization and Mapping (SLAM). Existing indoor localization technologies generally used scene-specific 3D representations or were trained on specific datasets, making it challenging to balance accuracy and cost when applied to new scenes. Addressing this issue, this paper proposed a universal indoor visual localization method based on efficient image retrieval. Initially, a Multi-Layer Perceptron (MLP) was employed to aggregate features from intermediate layers of a convolutional neural network, obtaining a global representation of the image. This approach ensured accurate and rapid retrieval of reference images. Subsequently, a new mechanism using Random Sample Consensus (RANSAC) was designed to resolve relative pose ambiguity caused by the essential matrix decomposition based on the five-point method. Finally, the absolute pose of the queried user image was computed, thereby achieving indoor user pose estimation. The proposed indoor localization method was characterized by its simplicity, flexibility, and excellent cross-scene generalization. Experimental results demonstrated a positioning error of 0.09 m and 2.14° on the 7Scenes dataset, and 0.15 m and 6.37° on the 12Scenes dataset. These results convincingly illustrated the outstanding performance of the proposed indoor localization method.

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.

Comprehensive Analysis of Indoor Formaldehyde Removal Techniques:Exploring Physical,Chemical,and Biological Methods

This research focuses on the evaluation of diverse approaches for removing formaldehyde from indoor environments,which is a significant concern for indoor air quality.The study systematically examines physical,chemical,and biological methods to ascertain their effectiveness in formaldehyde mitigation.Physical methods,including air circulation and adsorption,particularly with activated carbon and molecular sieves,are assessed for their efficiency in various concentration scenarios.Chemical methods,such as photocatalytic oxidation using titanium dioxide and plasma technology,are analyzed for their ability to decompose formaldehyde into non-toxic substances.Additionally,biological methods involving plant purification and microbial transformation are explored for their eco-friendly and sustainable removal capabilities.The paper concludes that while each method has its merits,a combined approach may offer the most effective solution for reducing indoor formaldehyde levels.The study underscores the need for further research to integrate these methods in a practical,cost-effective,and environmentally sustainable manner,highlighting their potential to improve indoor air quality significantly.

A Semantic-Sensitive Approach to Indoor and Outdoor 3D Data Organization

Building model data organization is often programmed to solve a specific problem,resulting in the inability to organize indoor and outdoor 3D scenes in an integrated manner.In this paper,existing building spatial data models are studied,and the characteristics of building information modeling standards(IFC),city geographic modeling language(CityGML),indoor modeling language(IndoorGML),and other models are compared and analyzed.CityGML and IndoorGML models face challenges in satisfying diverse application scenarios and requirements due to limitations in their expression capabilities.It is proposed to combine the semantic information of the model objects to effectively partition and organize the indoor and outdoor spatial 3D model data and to construct the indoor and outdoor data organization mechanism of“chunk-layer-subobject-entrances-area-detail object.”This method is verified by proposing a 3D data organization method for indoor and outdoor space and constructing a 3D visualization system based on it.

Strong Tracking Particle Filter Based on the Chi-Square Test for Indoor Positioning

In recent years,a number of wireless indoor positioning(WIP),such as Bluetooth,Wi-Fi,and Ultra-Wideband(UWB)technologies,are emerging.However,the indoor environment is complex and changeable.Walls,pillars,and even pedestrians may block wireless signals and produce non-line-of-sight(NLOS)deviations,resulting in decreased positioning accuracy and the inability to provide people with real-time continuous indoor positioning.This work proposed a strong tracking particle filter based on the chi-square test(SPFC)for indoor positioning.SPFC can fuse indoor wireless signals and the information of the inertial sensing unit(IMU)in the smartphone and detect the NLOS deviation through the chi-square test to avoid the influence of the NLOS deviation on the final positioning result.Simulation experiment results show that the proposed SPFC can reduce the positioning error by 15.1%and 12.3% compared with existing fusion positioning systems in the LOS and NLOS environment.

Research Status and Trends of Indoor Positioning and Navigation Technology in China

As an essential component of future comprehensive Positioning,Navigation,and Timing(PNT)system,indoor positioning technology has extensive application demands,making it a focal point of attention in both academia and industry.This article comprehensively reviews the research status of indoor positioning technology in China,with a focus on highlighting representative achievements and application validations from major research institutions in recent years.It addresses the challenges and issues faced in promotion and application of large-scale,high-precision indoor positioning.Furthermore,a universal and seamless indoor-outdoor positioning system architecture is proposed,along with a technical roadmap and key technologies to achieve this architecture.Finally,an analysis and outlook on future technological trends are presented.

A Robust Indoor Localization Algorithm Based on Polynomial Fitting and Gaussian Mixed Model

Wireless sensor network(WSN)positioning has a good effect on indoor positioning,so it has received extensive attention in the field of positioning.Non-line-of sight(NLOS)is a primary challenge in indoor complex environment.In this paper,a robust localization algorithm based on Gaussian mixture model and fitting polynomial is proposed to solve the problem of NLOS error.Firstly,fitting polynomials are used to predict the measured values.The residuals of predicted and measured values are clustered by Gaussian mixture model(GMM).The LOS probability and NLOS probability are calculated according to the clustering centers.The measured values are filtered by Kalman filter(KF),variable parameter unscented Kalman filter(VPUKF)and variable parameter particle filter(VPPF)in turn.The distance value processed by KF and VPUKF and the distance value processed by KF,VPUKF and VPPF are combined according to probability.Finally,the maximum likelihood method is used to calculate the position coordinate estimation.Through simulation comparison,the proposed algorithm has better positioning accuracy than several comparison algorithms in this paper.And it shows strong robustness in strong NLOS environment.

An Improved Hybrid Indoor Positioning Algorithm via QPSO and MLP Signal Weighting

Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units,in large indoor spaces demands a precise knowledge of their positions.Technologies like WiFi and Bluetooth,despite their low-cost and availability,are sensitive to signal noise and fading effects.For these reasons,a hybrid approach,which uses two different signal sources,has proven to be more resilient and accurate for the positioning determination in indoor environments.Hence,this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning,using Received Signal Strength information from available Wireless Local Area Network access points,together with the Wireless Sensor Networks technology.Six signals were recorded on a regular grid of anchor points,covering the research space.An optimization was performed by relative signal weighting,to minimize the average positioning error over the research space.The optimization process was conducted using a standard Quantum Particle Swarm Optimization,while the position error estimate for all given sets of weighted signals was performed using aMultilayer Perceptron(MLP)neural network.Compared to our previous research works,the MLP architecture was improved to three hidden layers and its learning parameters were finely tuned.These experimental results led to the 20%reduction of the positioning error when a suitable set of signal weights was calculated in the optimization process.Our final achieved value of 0.725 m of the location incertitude shows a sensible improvement compared to our previous results.

Introducing oxygen vacancies in TiO_(2) lattice through trivalent iron to enhance the photocatalytic removal of indoor NO

The synthesis of oxygen vacancies(OVs)-modified TiO_(2)under mild conditions is attractive.In this work,OVs were easily introduced in TiO_(2)lattice during the hydrothermal doping process of trivalent iron ions.Theoretical calculations based on a novel charge-compensation structure model were employed with experimental methods to reveal the intrinsic photocatalytic mechanism of Fe-doped TiO_(2)(Fe-TiO_(2)).The OVs formation energy in Fe-TiO_(2)(1.12 eV)was only 23.6%of that in TiO_(2)(4.74 eV),explaining why Fe^(3+)doping could introduce OVs in the TiO_(2)lattice.The calculation results also indicated that impurity states introduced by Fe^(3+)and OVs enhanced the light absorption activity of TiO_(2).Additionally,charge carrier transport was investigated through the carrier lifetime and relative mass.The carrier lifetime of Fe-TiO_(2)(4.00,4.10,and 3.34 ns for 1at%,2at%,and 3at%doping contents,respectively)was longer than that of undoped TiO_(2)(3.22 ns),indicating that Fe^(3+) and OVs could promote charge carrier separation,which can be attributed to the larger relative effective mass of electrons and holes.Herein,Fe-TiO_(2)has higher photocatalytic indoor NO removal activity compared with other photocatalysts because it has strong light absorption activity and high carrier separation efficiency.