Microorganisms constitute an essential component in the indoor environment,which is closely related to hu-man health.However,there is limited evidence regarding the associations between indoor airborne microbiome and ...Microorganisms constitute an essential component in the indoor environment,which is closely related to hu-man health.However,there is limited evidence regarding the associations between indoor airborne microbiome and systemic inflammation,as well as whether this association is modified by indoor particulate matter and the underlying mechanisms.In this prospective repeated-measure study among 66 participants,indoor airborne mi-crobiome was characterized using amplicon sequencing and qPCR.Indoor fine particulate matter(PM_(2.5))and inhalable particulate matter(PM10)were measured.Systemic inflammatory biomarkers were assessed,including white blood cell(WBC),neutrophil(NEUT),monocyte,eosinophil counts,and their proportions.Targeted serum amino acid metabolomics were conducted to explore the underlying mechanisms.Linear mixed-effect models re-vealed that bacterial and fungal Simpson diversity were significantly associated with decreased WBC and NEUT.For example,for each interquartile range increase in the bacterial Simpson diversity,WBC and NEUT changed by-4.53%(95%CI:-8.25%,-0.66%)and-5.95%(95%CI:-11.3%,-0.27%),respectively.Notably,increased inflammatory risks of airborne microbial exposure were observed when indoor PM_(2.5) and PM10 levels were below the WHO air quality guidelines.Mediation analyses indicated that dopamine metabolism partially mediated the anti-inflammatory effects of fungal diversity exposure.Overall,our study indicated protection from a diverse indoor microbial environment on cardiovascular health and proposed an underlying mechanism through amino acid metabolism.Additionally,health risks associated with microbial exposure deserve more attention in con-texts of low indoor particulate matter pollution.Further research is necessary to fully disentangle the complex relationships between indoor microbiome,air pollutants,and human health.展开更多
Although multicrystalline Si photovoltaics have been extensively studied and applied in the collection of solar energy,the same systems suffer significant efficiency losses in indoor settings,where ambient light condi...Although multicrystalline Si photovoltaics have been extensively studied and applied in the collection of solar energy,the same systems suffer significant efficiency losses in indoor settings,where ambient light conditions are considerably smaller in intensity and possess greater components of non-normal incidence.Yet,indoor light-driven,stand-alone devices can offer sustainable advances in next-generation technologies such as the Internet of Things.Here,we present a non-invasive solution to aid in photovoltaic indoor light collection—radially distributed waveguide-encoded lattice(RDWEL)slim films(thickness 1.5 mm).Embedded with a monotonical radial array of cylindrical waveguides(±20°),the RDWEL demonstrates seamless light collection(FoV(fields of view)=74.5°)and imparts enhancements in JSC(short circuit current density)of 44%and 14%for indoor and outdoor lighting conditions,respectively,when coupled to a photovoltaic device and compared to an unstructured but otherwise identical slim film coating.展开更多
The impact of location services on people’s lives has grown significantly in the era of widespread smart device usage.Due to global navigation satellite system(GNSS)signal rejection,weak signal strength in indoor env...The impact of location services on people’s lives has grown significantly in the era of widespread smart device usage.Due to global navigation satellite system(GNSS)signal rejection,weak signal strength in indoor environments and radio signal interference caused by multiwall environments,which collectively lead to significant positioning errors,vision-based positioning has emerged as a crucial method in indoor positioning research.This paper introduces a scale hierarchical matching model to tackle challenges associated with large visual databases and high scene similarity,both of which will compromise matching accuracy and lead to prolonged positioning delays.The proposed model establishes an image feature database using GIST features and speeded up robust feature(SURF)in the offline stage.In the online stage,a positioning navigating algorithm is constructed based on Dijkstra’s path planning.Additionally,a corresponding Android application has been developed to facilitate visual positioning and navigation in indoor environments.Experimental results obtained in real indoor environments demonstrate that the proposed method significantly enhances positioning accuracy compared with similar algorithms,while effectively reducing time overhead.This improvement caters to the requirements for indoor positioning and navigation,thereby meeting user needs.展开更多
Synthetic phenolic antioxidants(SPAs)are widely used in diverse industries due to their exceptional antioxidant characteristics.However,human exposure to SPAs may cause health problems.In this study,226 dust samples w...Synthetic phenolic antioxidants(SPAs)are widely used in diverse industries due to their exceptional antioxidant characteristics.However,human exposure to SPAs may cause health problems.In this study,226 dust samples were collected from 10 provinces in China,and six SPAs(three parent SPAs and their three transformation products)were analyzed.The concentrations of6SPAs(the sum of six target compounds)ranged from 15.4 to 3210 ng/g(geometric mean(GM):169 ng/g).The highest concentration of6SPAswas found in Sichuan Province(GM:349 ng/g),which was approximately 4 times higher than that in Hubei Province(81.6 ng/g)(p<0.05).The concentrations of butylated hydroxytoluene(BHT),2,2'-methylene bis(4-methyl-6–tert-butylphenol)(AO2246),2,6-di–tert–butyl–1,4-benzoquinone(BHT-Q),2,6-di–tert–butyl–4-(hydroxymethyl)phenol(BHT-OH),and ∑_(p)-SPAs were substantially higher in dust from urban areas than rural areas(p<0.05).AO2246 concentration in dust from homes(GM:0.400 ng/g)was about 4 times higher than that in workplaces(0.116 ng/g)(p<0.01).Significantly higherp-SPAs concentrations were found in dust from homes(GM:17.5 ng/g)than workplaces(11.4 ng/g)(p<0.01).The estimated daily intakes(EDIs)of ∑_(6)SPAs exposed through dust ingestion were 0.582,0.342,0.197,0.076,and 0.080 ng/kg bw/day in different age groups,and exposed through dermal contact was 0.358,0.252,0.174,0.167,and 0.177 ng/kg bw/day.EDIs showed that the exposure risks of SPAs decreased with age.This is the first work to determine SPAs in dust from10 provinces in China and investigate the spatial distribution of SPAs in those regions.展开更多
Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication(ISAC)systems,enabling precise navigation in environments where Global Positioning System(GPS)signals are unavailable...Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication(ISAC)systems,enabling precise navigation in environments where Global Positioning System(GPS)signals are unavailable.Existing methods,such as map-based navigation or site-specific fingerprinting,often require intensive data collection and lack generalization capability across different buildings,thereby limiting scalability.This study proposes a cross-site,map-free indoor localization framework that uses low-frequency sub-1 GHz radio signals and a Transformer-based neural network for robust positioning without prior environmental knowledge.The Transformer’s self-attention mechanisms allow it to capture spatial correlations among anchor nodes,facilitating accurate localization in unseen environments.Evaluation across two validation sites demonstrates the framework’s effectiveness.In crosssite testing(Site-A),the Transformer achieved a mean localization error of 9.44 m,outperforming the Deep Neural Network(DNN)(10.76 m)and Convolutional Neural Network(CNN)(12.02 m)baselines.In a real-time deployment(Site-B)spanning three floors,the Transformer maintained an overall mean error of 9.81 m,compared with 13.45 m for DNN,12.88 m for CNN,and 53.08 m for conventional trilateration.For vertical positioning,the Transformer delivered a mean error of 4.52 m,exceeding the performance of DNN(4.59 m),CNN(4.87 m),and trilateration(>45 m).The results confirm that the Transformer-based framework generalizes across heterogeneous indoor environments without requiring site-specific calibration,providing stable,sub-12 m horizontal accuracy and reliable vertical estimation.This capability makes the framework suitable for real-time applications in smart buildings,emergency response,and autonomous systems.By utilizing multipath reflections as an informative structure rather than treating them as noise,this work advances artificial intelligence(AI)-native indoor localization as a scalable and efficient component of future 6G ISAC networks.展开更多
With the increasing demand for indoor localization,indoor location based on Wi-Fi has gained wide attention due to its convenience of access.In this paper,we propose a new multi-feature fusion convolutional neural net...With the increasing demand for indoor localization,indoor location based on Wi-Fi has gained wide attention due to its convenience of access.In this paper,we propose a new multi-feature fusion convolutional neural network(CNN)based on channel state information(CSI)images,which contains more feature information by constituting a new CSI image with amplitude and angle of arrival information of CSI information collected at known points.Moreover,the global mean filtering(GMC)algorithm with median filtering proposed in this paper is used to filter and reduce the noise of CSI images to obtain clearer images for network training.To extract more features from the CSI images,the traditional single-channel network is extended,and a two-channel design is introduced to extract feature information between adjacent subcarriers.Experimental evaluation is performed in a typical indoor environment,and the proposed method is experimentally proven to have good localization performance.展开更多
Biometric characteristics are playing a vital role in security for the last few years.Human gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework...Biometric characteristics are playing a vital role in security for the last few years.Human gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework for human gait classification in video sequences using deep learning(DL)fusion assisted and posterior probability-based moth flames optimization(MFO)is proposed.In the first step,the video frames are resized and finetuned by two pre-trained lightweight DL models,EfficientNetB0 and MobileNetV2.Both models are selected based on the top-5 accuracy and less number of parameters.Later,both models are trained through deep transfer learning and extracted deep features fused using a voting scheme.In the last step,the authors develop a posterior probabilitybased MFO feature selection algorithm to select the best features.The selected features are classified using several supervised learning methods.The CASIA-B publicly available dataset has been employed for the experimental process.On this dataset,the authors selected six angles such as 0°,18°,90°,108°,162°,and 180°and obtained an average accuracy of 96.9%,95.7%,86.8%,90.0%,95.1%,and 99.7%.Results demonstrate comparable improvement in accuracy and significantly minimize the computational time with recent state-of-the-art techniques.展开更多
Indoor visual localization relies heavily on image retrieval to ascertain location information.However,the widespread presence and high consistency of floor patterns across different images of-ten lead to the extracti...Indoor visual localization relies heavily on image retrieval to ascertain location information.However,the widespread presence and high consistency of floor patterns across different images of-ten lead to the extraction of numerous repetitive features,thereby reducing the accuracy of image retrieval.This article proposes an indoor visual localization method based on semantic segmentation and adaptive weight fusion to address the issue of ground texture interference with retrieval results.During the positioning process,an indoor semantic segmentation model is established.Semantic segmentation technology is applied to accurately delineate the ground portion of the images.Fea-ture extraction is performed on both the original database and the ground-segmented database.The vector of locally aggregated descriptors(VLAD)algorithm is then used to convert image features into a fixed-length feature representation,which improves the efficiency of image retrieval.Simul-taneously,a method for adaptive weight optimization in similarity calculation is proposed,using a-daptive weights to compute similarity for different regional features,thereby improving the accuracy of image retrieval.The experimental results indicate that this method significantly reduces ground interference and effectively utilizes ground information,thereby improving the accuracy of image retrieval.展开更多
Taking modern indoor building construction as an example,this study analyzes the path planning and navigation of a smart plastering robot.It includes a basic introduction to smart plastering robots,an analysis of mult...Taking modern indoor building construction as an example,this study analyzes the path planning and navigation of a smart plastering robot.It includes a basic introduction to smart plastering robots,an analysis of multi-sensor fusion localization algorithms for smart plastering robots,and an analysis of path planning and navigation functions for smart plastering robots.It is hoped that through this analysis,a reference is provided for the path planning and navigation design of such robots to meet their practical application needs.展开更多
Indoor air quality(IAQ)is often overlooked,yet a poorly maintained environment can lead to significant health issues and reduced concentration and productivity in work or educational settings.This study presents an in...Indoor air quality(IAQ)is often overlooked,yet a poorly maintained environment can lead to significant health issues and reduced concentration and productivity in work or educational settings.This study presents an innovative control system for mechanical ventilation specifically designed for university classrooms,with the dual goal of enhancing IAQ and increasing energy efficiency.Two classrooms with distinct construction characteristics were analyzed:one with exterior walls and windows,and the other completely underground.For each classroom,a model was developed using DesignBuilder software,which was calibrated with experimental data regarding CO_(2) concentration,temperature,and relative humidity levels.The proposed ventilation system operates based on CO_(2) concentration,relative humidity,and potential for free heating and cooling.In addition,the analysis was conducted for other locations,demonstrating consistent energy savings across different climates and environments,always showing an annual reduction in energy consumption.Results demonstrate that mechanical ventilation,when integrated with heat recovery and free cooling strategies,significantly reduces energy consumption by up to 25%,while also maintaining optimal CO_(2) levels to enhance comfort and air quality.These findings emphasize the essential need for well-designed mechanical ventilation systems to ensure both psychophysical well-being and IAQ in enclosed spaces,particularly in environments intended for extended occupancy,such as classrooms.Furthermore,this approach has broad applicability,as it could be adapted to various building types,thereby contributing to sustainable energy management practices and promoting healthier indoor spaces.This study serves as a model for future designs aiming to balance energy efficiency with indoor air quality,especially relevant in the post-COVID era,where the importance of indoor air quality has become more widely recognized.展开更多
Bluetooth low energy(BLE)-based indoor localization has been extensively researched due to its cost-effectiveness,low power consumption,and ubiquity.Despite these advantages,the variability of received signal strength...Bluetooth low energy(BLE)-based indoor localization has been extensively researched due to its cost-effectiveness,low power consumption,and ubiquity.Despite these advantages,the variability of received signal strength indicator(RSSI)measurements,influenced by physical obstacles,human presence,and electronic interference,poses a significant challenge to accurate localization.In this work,we present an optimised method to enhance indoor localization accuracy by utilising multiple BLE beacons in a radio frequency(RF)-dense modern building environment.Through a proof-of-concept study,we demonstrate that using three BLE beacons reduces localization error from a worst-case distance of 9.09-2.94 m,whereas additional beacons offer minimal incremental benefit in such settings.Furthermore,our framework for BLE-based localization,implemented on an edge network of Raspberry Pies,has been released under an open-source license,enabling broader application and further research.展开更多
Heat Recovery Ventilators(HRVs)are essential for improving indoor air quality(IAQ)and reducing energy consumption in residential buildings situated in cold climates.This study considers the efficiency and performance ...Heat Recovery Ventilators(HRVs)are essential for improving indoor air quality(IAQ)and reducing energy consumption in residential buildings situated in cold climates.This study considers the efficiency and performance optimization of HRVs under cold climatic conditions,where conventional ventilation systems increase heat loss.A comprehensive numerical model was developed using COMSOL Multiphysics,integrating fluid dynamics,heat transfer,and solid mechanics to evaluate the thermal efficiency and structural integrity of an HRV system.The methodology employed a detailed geometry with tetrahedral elements,temperature-dependent material properties,and coupled governing equations solved under Tehran-specific boundary conditions.A multi-objective optimization was implemented in the framework of the Nelder-Mead simplex algorithm,targeting the maximization of the average outlet temperature and minimization of the maximum von Mises thermal stress,with inlet flow velocity as the design variable(range:0.5–1.2m/s).Results indicate an optimal velocity of 0.51563 m/s,achieving an average outlet temperature of 289.44 K and maximum von Mises stress of 221 MPa,validated through mesh independence and detailed contour analyses of temperature,velocity,and stress distributions.展开更多
This article analyses the relationship between PM_(10) concentrations inside and outside two schools in Barreiro,Portugal:Primary School No.5 and D.Luís Mendonça Furtado Basic School.The main objective was t...This article analyses the relationship between PM_(10) concentrations inside and outside two schools in Barreiro,Portugal:Primary School No.5 and D.Luís Mendonça Furtado Basic School.The main objective was to understand the impact of external and internal sources on indoor air quality(IAQ)in school environments.Monitoring campaigns were carried out in different indoor spaces,including classrooms,the gym,and the canteen,and the results were compared with PM_(10) levels outside the building.At Primary School No.5,indoor PM10 concentrations were consistently higher than the outdoor values measured on Avenida do Bocage,with an average Indoor/Outdoor(I/O)ratio of 2.2,indicating a significant impact of indoor activities on particle levels.Similarly,at the D.Luís Mendonça Furtado Basic School,there was an increase in PM_(10) and PM_(2:5) concentrations during school hours,with the highest I/O ratio(3.04)recorded on school days.In the evenings and at weekends,when the spaces were unoccupied,particle concentrations dropped considerably,reaching an I/O ratio of 0.70.Said results suggest that indoor activities are a determining factor for particle levels in indoor air,emphasizing the need for ventilation and pollution control strategies in schools to protect the health of students and staff.展开更多
Background Advancements in computer science and knowledge have made the incorporation of control theory,graphics processing,and mathematical models increasingly important for urban design planning.However,challenges r...Background Advancements in computer science and knowledge have made the incorporation of control theory,graphics processing,and mathematical models increasingly important for urban design planning.However,challenges remain in aligning virtual reality(VR)environments with real-world spatial and preparation requirements,particularly in indoor urban spaces.Methods This study investigates the application of VR technology to urban design,focusing on the growth and assessment of the redirection of the space-tree sorter algorithm(STSA).It outlines various assessment indicators,organization of the VR-based system architecture,and construction of 3D urban models and databases.This research also examined methods for the interactive adjustment of indoor space layout plans within a VR environment.Results This research study involved developing and demonstrating the creation and simulation of urban indoor spaces and cityscapes in VR and implementing an experimental setup to test layout modifications and system interactivity.The results indicated enhanced alignment between the virtual and physical spatial configurations.The analysis highlights the strengths and limitations of current VR systems for urban design and identifies key areas for optimization and refinement.Conclusion High congruence between virtual simulations and real-world urban spaces is necessary for effective VR-driven urban planning.This study contributes to a clearer understanding of how 3D modeling,interactive layout design,and reproduction technology can be efficiently employed to support urban increase initiatives.展开更多
As emerging services continue to be explored,indoor communications geared towards different user requirements will face severe challenges such as larger penetration losses and more critical multipath issues,leading to...As emerging services continue to be explored,indoor communications geared towards different user requirements will face severe challenges such as larger penetration losses and more critical multipath issues,leading to difficulties in achieving flexible coverage.In this paper,we introduce transmissive reconfigurable intelligent surfaces(RISs)as intelligent passive auxiliary devices into indoor scenes,replacing conventional ultra-dense small cell and relay forwarding approaches to address these issues at low deployment and operation costs.Specifically,we study the optimization design of active and passive beamforming for the transmissive RISs-aided indoor multiuser downlink communication systems.This involves considering more realistic indoor congestion modeling and near-field propagation characteristics.The goal of our optimization is to minimize the total transmit power at the access point(AP)for different user service requirements,including quality-of-service(QoS)and wireless power transfer(WPT).Due to the nonconvex nature of the optimization problem,adaptive penalty coefficients are imported to solve it alternatively with closed-form solutions for both active and passive beamforming.Simulation results demonstrate that the use of transmissive RISs is indeed an efficient way to achieve flexible coverage in indoor scenarios.Furthermore,the proposed optimization algorithm has been proven to be effective and robust in achieving energy-saving transmission.展开更多
This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and i...This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.展开更多
To improve the accuracy of indoor localization methods with channel state information(CSI)images,a localization method that used CSI images from selected multiple access points(APs)was proposed.The method had an off-l...To improve the accuracy of indoor localization methods with channel state information(CSI)images,a localization method that used CSI images from selected multiple access points(APs)was proposed.The method had an off-line phase and an on-line phase.In the off-line phase,three APs were selected from the four APs in the localization area based on the received signal strength indication(RSSI).Next,CSI data was collected from the three selected APs using a commercial Intel 5300 network interface card.A single-channel subimage was constructed for each selected AP by combining the amplitude information from different antennas and the phase difference information between neighboring antennas.These sub-images were then merged to form a three-channel RGB image,which was subsequently fed into the convolutional neural network(CNN)for training.The CNN model was saved upon completion of training.In the on-line phase,the CSI data from the target device was collected,converted into images using the same process as in the off-line phase,and fed into the well-trained CNN model.Finally,the real position of the target device was estimated using a weighted centroid algorithm based on the model’s output probabilities.The proposed method was validated in indoor environments using two datasets,achieving good localization accuracy.展开更多
Negative air ions(NAIs)in indoor environments have been suggested to positively impact human health by effectively reducing particulate contamination and gaseous pollutants,as well as inhibiting the growth of microorg...Negative air ions(NAIs)in indoor environments have been suggested to positively impact human health by effectively reducing particulate contamination and gaseous pollutants,as well as inhibiting the growth of microorganisms,bacteria and viruses.This study investigates the common ionizers with different module types,and the mechanism of NAIs for enhancing indoor air quality,as well as the positive and negative impacts on human health.The association between NAI concentrations and human health outcomes is examined,and alternative measures to balance beneficial and unavailing effects are investigated.While NAIs demonstrate efficacy in removing particulate pollutants,alleviating depression,enhancing cognitive function and even stimulating sympathetic activity,it is pertinent to acknowledge the presence of contradictory findings concerning their effects on cardiac autonomic function and respiratory physiology.To address this complexity,it is imperative to consider alternative measures that strike a balance between the beneficial and unavailing effects of NAIs.These measures can encompass a general assessment of the characteristics of particulate pollutants,a strategic selection of ionizer technologies,and adherence to the recommended optimal concentration thresholds of NAIs.展开更多
To improve the quality of the illumination distribution,one novel indoor visible light communication(VLC)system,which is jointly assisted by the angle-diversity transceivers and simultaneous transmission and reflectio...To improve the quality of the illumination distribution,one novel indoor visible light communication(VLC)system,which is jointly assisted by the angle-diversity transceivers and simultaneous transmission and reflection-intelligent reflecting surface(STAR-IRS),has been proposed in this work.A Harris Hawks optimizer algorithm(HHOA)-based two-stage alternating iteration algorithm(TSAIA)is presented to jointly optimize the magnitude and uniformity of the received optical power.Besides,to demonstrate the superiority of the proposed strategy,several benchmark schemes are simulated and compared.Results showed that compared to other optimization strategies,the TSAIA scheme is more capable of balancing the average value and variance of the received optical power,when the maximal ratio combining(MRC)strategy is adopted at the receiver.Moreover,as the number of the STAR-IRS elements increases,the optical power variance of the system optimized by TSAIA scheme would become smaller while the average optical power would get larger.This study will benefit the design of received optical power distribution for indoor VLC systems.展开更多
基金supported by the National Key Research and Development Program of China(No.2022YFC3702704)the National Natural Science Foundation of China(Nos.22376005,22076006 and 82073506).
文摘Microorganisms constitute an essential component in the indoor environment,which is closely related to hu-man health.However,there is limited evidence regarding the associations between indoor airborne microbiome and systemic inflammation,as well as whether this association is modified by indoor particulate matter and the underlying mechanisms.In this prospective repeated-measure study among 66 participants,indoor airborne mi-crobiome was characterized using amplicon sequencing and qPCR.Indoor fine particulate matter(PM_(2.5))and inhalable particulate matter(PM10)were measured.Systemic inflammatory biomarkers were assessed,including white blood cell(WBC),neutrophil(NEUT),monocyte,eosinophil counts,and their proportions.Targeted serum amino acid metabolomics were conducted to explore the underlying mechanisms.Linear mixed-effect models re-vealed that bacterial and fungal Simpson diversity were significantly associated with decreased WBC and NEUT.For example,for each interquartile range increase in the bacterial Simpson diversity,WBC and NEUT changed by-4.53%(95%CI:-8.25%,-0.66%)and-5.95%(95%CI:-11.3%,-0.27%),respectively.Notably,increased inflammatory risks of airborne microbial exposure were observed when indoor PM_(2.5) and PM10 levels were below the WHO air quality guidelines.Mediation analyses indicated that dopamine metabolism partially mediated the anti-inflammatory effects of fungal diversity exposure.Overall,our study indicated protection from a diverse indoor microbial environment on cardiovascular health and proposed an underlying mechanism through amino acid metabolism.Additionally,health risks associated with microbial exposure deserve more attention in con-texts of low indoor particulate matter pollution.Further research is necessary to fully disentangle the complex relationships between indoor microbiome,air pollutants,and human health.
基金supported by the European Research Council(ERC)under the European Union's Horizon 2020 Research and Innovation Programme(Grant Agreement No.818762)the Engineering and Physical Sciences Research Council(Grant No.EP/V048953/1)and the Isaac Newton Trust(grant 22.39(m))。
文摘Although multicrystalline Si photovoltaics have been extensively studied and applied in the collection of solar energy,the same systems suffer significant efficiency losses in indoor settings,where ambient light conditions are considerably smaller in intensity and possess greater components of non-normal incidence.Yet,indoor light-driven,stand-alone devices can offer sustainable advances in next-generation technologies such as the Internet of Things.Here,we present a non-invasive solution to aid in photovoltaic indoor light collection—radially distributed waveguide-encoded lattice(RDWEL)slim films(thickness 1.5 mm).Embedded with a monotonical radial array of cylindrical waveguides(±20°),the RDWEL demonstrates seamless light collection(FoV(fields of view)=74.5°)and imparts enhancements in JSC(short circuit current density)of 44%and 14%for indoor and outdoor lighting conditions,respectively,when coupled to a photovoltaic device and compared to an unstructured but otherwise identical slim film coating.
基金Supported by the National Natural Science Foundation of China(No.61971162,61771186)the Natural Science Foundation of Heilongjiang Province(No.PL2024F025)+1 种基金the Open Research Fund of National Mobile Communications Research Laboratory in Southeast University(No.2023D07)the Fundamental Scientific Research Funds of Heilongjiang Province(No.2022-KYYWF-1050).
文摘The impact of location services on people’s lives has grown significantly in the era of widespread smart device usage.Due to global navigation satellite system(GNSS)signal rejection,weak signal strength in indoor environments and radio signal interference caused by multiwall environments,which collectively lead to significant positioning errors,vision-based positioning has emerged as a crucial method in indoor positioning research.This paper introduces a scale hierarchical matching model to tackle challenges associated with large visual databases and high scene similarity,both of which will compromise matching accuracy and lead to prolonged positioning delays.The proposed model establishes an image feature database using GIST features and speeded up robust feature(SURF)in the offline stage.In the online stage,a positioning navigating algorithm is constructed based on Dijkstra’s path planning.Additionally,a corresponding Android application has been developed to facilitate visual positioning and navigation in indoor environments.Experimental results obtained in real indoor environments demonstrate that the proposed method significantly enhances positioning accuracy compared with similar algorithms,while effectively reducing time overhead.This improvement caters to the requirements for indoor positioning and navigation,thereby meeting user needs.
基金supported by the National Key Research and Development Program of China(No.2023YFC3706602)the National Natural Science Foundation of China(Nos.22225605 and 22193051)the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB0750200).
文摘Synthetic phenolic antioxidants(SPAs)are widely used in diverse industries due to their exceptional antioxidant characteristics.However,human exposure to SPAs may cause health problems.In this study,226 dust samples were collected from 10 provinces in China,and six SPAs(three parent SPAs and their three transformation products)were analyzed.The concentrations of6SPAs(the sum of six target compounds)ranged from 15.4 to 3210 ng/g(geometric mean(GM):169 ng/g).The highest concentration of6SPAswas found in Sichuan Province(GM:349 ng/g),which was approximately 4 times higher than that in Hubei Province(81.6 ng/g)(p<0.05).The concentrations of butylated hydroxytoluene(BHT),2,2'-methylene bis(4-methyl-6–tert-butylphenol)(AO2246),2,6-di–tert–butyl–1,4-benzoquinone(BHT-Q),2,6-di–tert–butyl–4-(hydroxymethyl)phenol(BHT-OH),and ∑_(p)-SPAs were substantially higher in dust from urban areas than rural areas(p<0.05).AO2246 concentration in dust from homes(GM:0.400 ng/g)was about 4 times higher than that in workplaces(0.116 ng/g)(p<0.01).Significantly higherp-SPAs concentrations were found in dust from homes(GM:17.5 ng/g)than workplaces(11.4 ng/g)(p<0.01).The estimated daily intakes(EDIs)of ∑_(6)SPAs exposed through dust ingestion were 0.582,0.342,0.197,0.076,and 0.080 ng/kg bw/day in different age groups,and exposed through dermal contact was 0.358,0.252,0.174,0.167,and 0.177 ng/kg bw/day.EDIs showed that the exposure risks of SPAs decreased with age.This is the first work to determine SPAs in dust from10 provinces in China and investigate the spatial distribution of SPAs in those regions.
基金funded by the Ministry of Science and Technology,Taiwan,under grant number MOST 114-2224-E-A49-002was received by En-Cheng Liou.
文摘Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication(ISAC)systems,enabling precise navigation in environments where Global Positioning System(GPS)signals are unavailable.Existing methods,such as map-based navigation or site-specific fingerprinting,often require intensive data collection and lack generalization capability across different buildings,thereby limiting scalability.This study proposes a cross-site,map-free indoor localization framework that uses low-frequency sub-1 GHz radio signals and a Transformer-based neural network for robust positioning without prior environmental knowledge.The Transformer’s self-attention mechanisms allow it to capture spatial correlations among anchor nodes,facilitating accurate localization in unseen environments.Evaluation across two validation sites demonstrates the framework’s effectiveness.In crosssite testing(Site-A),the Transformer achieved a mean localization error of 9.44 m,outperforming the Deep Neural Network(DNN)(10.76 m)and Convolutional Neural Network(CNN)(12.02 m)baselines.In a real-time deployment(Site-B)spanning three floors,the Transformer maintained an overall mean error of 9.81 m,compared with 13.45 m for DNN,12.88 m for CNN,and 53.08 m for conventional trilateration.For vertical positioning,the Transformer delivered a mean error of 4.52 m,exceeding the performance of DNN(4.59 m),CNN(4.87 m),and trilateration(>45 m).The results confirm that the Transformer-based framework generalizes across heterogeneous indoor environments without requiring site-specific calibration,providing stable,sub-12 m horizontal accuracy and reliable vertical estimation.This capability makes the framework suitable for real-time applications in smart buildings,emergency response,and autonomous systems.By utilizing multipath reflections as an informative structure rather than treating them as noise,this work advances artificial intelligence(AI)-native indoor localization as a scalable and efficient component of future 6G ISAC networks.
基金supported by Natural Science Foundation of Hunan Province under Grant(NO:2021JJ31142).
文摘With the increasing demand for indoor localization,indoor location based on Wi-Fi has gained wide attention due to its convenience of access.In this paper,we propose a new multi-feature fusion convolutional neural network(CNN)based on channel state information(CSI)images,which contains more feature information by constituting a new CSI image with amplitude and angle of arrival information of CSI information collected at known points.Moreover,the global mean filtering(GMC)algorithm with median filtering proposed in this paper is used to filter and reduce the noise of CSI images to obtain clearer images for network training.To extract more features from the CSI images,the traditional single-channel network is extended,and a two-channel design is introduced to extract feature information between adjacent subcarriers.Experimental evaluation is performed in a typical indoor environment,and the proposed method is experimentally proven to have good localization performance.
基金King Saud University,Grant/Award Number:RSP2024R157。
文摘Biometric characteristics are playing a vital role in security for the last few years.Human gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework for human gait classification in video sequences using deep learning(DL)fusion assisted and posterior probability-based moth flames optimization(MFO)is proposed.In the first step,the video frames are resized and finetuned by two pre-trained lightweight DL models,EfficientNetB0 and MobileNetV2.Both models are selected based on the top-5 accuracy and less number of parameters.Later,both models are trained through deep transfer learning and extracted deep features fused using a voting scheme.In the last step,the authors develop a posterior probabilitybased MFO feature selection algorithm to select the best features.The selected features are classified using several supervised learning methods.The CASIA-B publicly available dataset has been employed for the experimental process.On this dataset,the authors selected six angles such as 0°,18°,90°,108°,162°,and 180°and obtained an average accuracy of 96.9%,95.7%,86.8%,90.0%,95.1%,and 99.7%.Results demonstrate comparable improvement in accuracy and significantly minimize the computational time with recent state-of-the-art techniques.
基金Supported by the National Natural Science Foundation of China(No.61971162,61771186)the Natural Science Foundation of Heilongjiang Province(No.PL2024F025)+2 种基金the Open Research Fund of National Mobile Communications Research Laboratory Southeast University(No.2023D07)the Outstanding Youth Program of Natural Science Foundation of Heilongjiang Province(No.YQ2020F012)the Funda-mental Scientific Research Funds of Heilongjiang Province(No.2022-KYYWF-1050).
文摘Indoor visual localization relies heavily on image retrieval to ascertain location information.However,the widespread presence and high consistency of floor patterns across different images of-ten lead to the extraction of numerous repetitive features,thereby reducing the accuracy of image retrieval.This article proposes an indoor visual localization method based on semantic segmentation and adaptive weight fusion to address the issue of ground texture interference with retrieval results.During the positioning process,an indoor semantic segmentation model is established.Semantic segmentation technology is applied to accurately delineate the ground portion of the images.Fea-ture extraction is performed on both the original database and the ground-segmented database.The vector of locally aggregated descriptors(VLAD)algorithm is then used to convert image features into a fixed-length feature representation,which improves the efficiency of image retrieval.Simul-taneously,a method for adaptive weight optimization in similarity calculation is proposed,using a-daptive weights to compute similarity for different regional features,thereby improving the accuracy of image retrieval.The experimental results indicate that this method significantly reduces ground interference and effectively utilizes ground information,thereby improving the accuracy of image retrieval.
基金Science and Technology Research Project of Chongqing Education Commission(Project No.:KJQN202401902)Chongqing Construction Science and Technology Plan Project(Project No.:Chinese Society For Urban Studies,2024:3-24)+1 种基金cientific Research Fund Project of Chongqing Institute of Engineering(Project No.:2022gcky01)College Student Innovation and Entrepreneurship Training Program Project of Chongqing Institute of Engineering(Project No.:CXCY2024018)。
文摘Taking modern indoor building construction as an example,this study analyzes the path planning and navigation of a smart plastering robot.It includes a basic introduction to smart plastering robots,an analysis of multi-sensor fusion localization algorithms for smart plastering robots,and an analysis of path planning and navigation functions for smart plastering robots.It is hoped that through this analysis,a reference is provided for the path planning and navigation design of such robots to meet their practical application needs.
基金Funding Statement:This research was conducted as part of the Tech4You Project“Technologies for climate change adaptation and quality of life improvement”,n.ECS0000009,CUP H23C22000370006,Italian PNRR,Mission 4,Component 2,Investment 1.5 funded by the European Union-NextGenerationEU.
文摘Indoor air quality(IAQ)is often overlooked,yet a poorly maintained environment can lead to significant health issues and reduced concentration and productivity in work or educational settings.This study presents an innovative control system for mechanical ventilation specifically designed for university classrooms,with the dual goal of enhancing IAQ and increasing energy efficiency.Two classrooms with distinct construction characteristics were analyzed:one with exterior walls and windows,and the other completely underground.For each classroom,a model was developed using DesignBuilder software,which was calibrated with experimental data regarding CO_(2) concentration,temperature,and relative humidity levels.The proposed ventilation system operates based on CO_(2) concentration,relative humidity,and potential for free heating and cooling.In addition,the analysis was conducted for other locations,demonstrating consistent energy savings across different climates and environments,always showing an annual reduction in energy consumption.Results demonstrate that mechanical ventilation,when integrated with heat recovery and free cooling strategies,significantly reduces energy consumption by up to 25%,while also maintaining optimal CO_(2) levels to enhance comfort and air quality.These findings emphasize the essential need for well-designed mechanical ventilation systems to ensure both psychophysical well-being and IAQ in enclosed spaces,particularly in environments intended for extended occupancy,such as classrooms.Furthermore,this approach has broad applicability,as it could be adapted to various building types,thereby contributing to sustainable energy management practices and promoting healthier indoor spaces.This study serves as a model for future designs aiming to balance energy efficiency with indoor air quality,especially relevant in the post-COVID era,where the importance of indoor air quality has become more widely recognized.
基金supported by James M.Cox Foundation,National Institute on Deafness and Other Communication Disorders(grant no.1R21DC021029-01A1)Cox Enterprises Inc.,National Institute of Child Health and Human Development(grant no.AWD-006196-G1)Thrasher Research Fund Early Career Award Program.
文摘Bluetooth low energy(BLE)-based indoor localization has been extensively researched due to its cost-effectiveness,low power consumption,and ubiquity.Despite these advantages,the variability of received signal strength indicator(RSSI)measurements,influenced by physical obstacles,human presence,and electronic interference,poses a significant challenge to accurate localization.In this work,we present an optimised method to enhance indoor localization accuracy by utilising multiple BLE beacons in a radio frequency(RF)-dense modern building environment.Through a proof-of-concept study,we demonstrate that using three BLE beacons reduces localization error from a worst-case distance of 9.09-2.94 m,whereas additional beacons offer minimal incremental benefit in such settings.Furthermore,our framework for BLE-based localization,implemented on an edge network of Raspberry Pies,has been released under an open-source license,enabling broader application and further research.
文摘Heat Recovery Ventilators(HRVs)are essential for improving indoor air quality(IAQ)and reducing energy consumption in residential buildings situated in cold climates.This study considers the efficiency and performance optimization of HRVs under cold climatic conditions,where conventional ventilation systems increase heat loss.A comprehensive numerical model was developed using COMSOL Multiphysics,integrating fluid dynamics,heat transfer,and solid mechanics to evaluate the thermal efficiency and structural integrity of an HRV system.The methodology employed a detailed geometry with tetrahedral elements,temperature-dependent material properties,and coupled governing equations solved under Tehran-specific boundary conditions.A multi-objective optimization was implemented in the framework of the Nelder-Mead simplex algorithm,targeting the maximization of the average outlet temperature and minimization of the maximum von Mises thermal stress,with inlet flow velocity as the design variable(range:0.5–1.2m/s).Results indicate an optimal velocity of 0.51563 m/s,achieving an average outlet temperature of 289.44 K and maximum von Mises stress of 221 MPa,validated through mesh independence and detailed contour analyses of temperature,velocity,and stress distributions.
文摘This article analyses the relationship between PM_(10) concentrations inside and outside two schools in Barreiro,Portugal:Primary School No.5 and D.Luís Mendonça Furtado Basic School.The main objective was to understand the impact of external and internal sources on indoor air quality(IAQ)in school environments.Monitoring campaigns were carried out in different indoor spaces,including classrooms,the gym,and the canteen,and the results were compared with PM_(10) levels outside the building.At Primary School No.5,indoor PM10 concentrations were consistently higher than the outdoor values measured on Avenida do Bocage,with an average Indoor/Outdoor(I/O)ratio of 2.2,indicating a significant impact of indoor activities on particle levels.Similarly,at the D.Luís Mendonça Furtado Basic School,there was an increase in PM_(10) and PM_(2:5) concentrations during school hours,with the highest I/O ratio(3.04)recorded on school days.In the evenings and at weekends,when the spaces were unoccupied,particle concentrations dropped considerably,reaching an I/O ratio of 0.70.Said results suggest that indoor activities are a determining factor for particle levels in indoor air,emphasizing the need for ventilation and pollution control strategies in schools to protect the health of students and staff.
文摘Background Advancements in computer science and knowledge have made the incorporation of control theory,graphics processing,and mathematical models increasingly important for urban design planning.However,challenges remain in aligning virtual reality(VR)environments with real-world spatial and preparation requirements,particularly in indoor urban spaces.Methods This study investigates the application of VR technology to urban design,focusing on the growth and assessment of the redirection of the space-tree sorter algorithm(STSA).It outlines various assessment indicators,organization of the VR-based system architecture,and construction of 3D urban models and databases.This research also examined methods for the interactive adjustment of indoor space layout plans within a VR environment.Results This research study involved developing and demonstrating the creation and simulation of urban indoor spaces and cityscapes in VR and implementing an experimental setup to test layout modifications and system interactivity.The results indicated enhanced alignment between the virtual and physical spatial configurations.The analysis highlights the strengths and limitations of current VR systems for urban design and identifies key areas for optimization and refinement.Conclusion High congruence between virtual simulations and real-world urban spaces is necessary for effective VR-driven urban planning.This study contributes to a clearer understanding of how 3D modeling,interactive layout design,and reproduction technology can be efficiently employed to support urban increase initiatives.
基金supported in part by the Natural Science Basic Research Plan in Shaanxi Province under Grant 2024JC-ZDXM-36in part by the Key Research and Development Program of Shaanxi Province under Grant 2023-YBGY-255+2 种基金in part by the Excellent Youth Science Foundation of Xi’an University of Science and Technology under Grant 2019YQ3-13in part by the Xi’an Key Laboratory of Network Convergence Communications under Grant 2022NCC-K102in part by the Fundamental Research Funds for the Central Universities under Grant QTZX23029。
文摘As emerging services continue to be explored,indoor communications geared towards different user requirements will face severe challenges such as larger penetration losses and more critical multipath issues,leading to difficulties in achieving flexible coverage.In this paper,we introduce transmissive reconfigurable intelligent surfaces(RISs)as intelligent passive auxiliary devices into indoor scenes,replacing conventional ultra-dense small cell and relay forwarding approaches to address these issues at low deployment and operation costs.Specifically,we study the optimization design of active and passive beamforming for the transmissive RISs-aided indoor multiuser downlink communication systems.This involves considering more realistic indoor congestion modeling and near-field propagation characteristics.The goal of our optimization is to minimize the total transmit power at the access point(AP)for different user service requirements,including quality-of-service(QoS)and wireless power transfer(WPT).Due to the nonconvex nature of the optimization problem,adaptive penalty coefficients are imported to solve it alternatively with closed-form solutions for both active and passive beamforming.Simulation results demonstrate that the use of transmissive RISs is indeed an efficient way to achieve flexible coverage in indoor scenarios.Furthermore,the proposed optimization algorithm has been proven to be effective and robust in achieving energy-saving transmission.
文摘This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.
基金supported by Lanzhou Science and Technology Plan Project(No.2023-3-104)Gansu Province Higher Education Industry Support Plan Project(No.2023CYZC-40)Gansu Province Excellent Graduate“Innovation Star”Program(No.2023CXZX-546)。
文摘To improve the accuracy of indoor localization methods with channel state information(CSI)images,a localization method that used CSI images from selected multiple access points(APs)was proposed.The method had an off-line phase and an on-line phase.In the off-line phase,three APs were selected from the four APs in the localization area based on the received signal strength indication(RSSI).Next,CSI data was collected from the three selected APs using a commercial Intel 5300 network interface card.A single-channel subimage was constructed for each selected AP by combining the amplitude information from different antennas and the phase difference information between neighboring antennas.These sub-images were then merged to form a three-channel RGB image,which was subsequently fed into the convolutional neural network(CNN)for training.The CNN model was saved upon completion of training.In the on-line phase,the CSI data from the target device was collected,converted into images using the same process as in the off-line phase,and fed into the well-trained CNN model.Finally,the real position of the target device was estimated using a weighted centroid algorithm based on the model’s output probabilities.The proposed method was validated in indoor environments using two datasets,achieving good localization accuracy.
基金Research Project of Marine Design and Research Institute of China。
文摘Negative air ions(NAIs)in indoor environments have been suggested to positively impact human health by effectively reducing particulate contamination and gaseous pollutants,as well as inhibiting the growth of microorganisms,bacteria and viruses.This study investigates the common ionizers with different module types,and the mechanism of NAIs for enhancing indoor air quality,as well as the positive and negative impacts on human health.The association between NAI concentrations and human health outcomes is examined,and alternative measures to balance beneficial and unavailing effects are investigated.While NAIs demonstrate efficacy in removing particulate pollutants,alleviating depression,enhancing cognitive function and even stimulating sympathetic activity,it is pertinent to acknowledge the presence of contradictory findings concerning their effects on cardiac autonomic function and respiratory physiology.To address this complexity,it is imperative to consider alternative measures that strike a balance between the beneficial and unavailing effects of NAIs.These measures can encompass a general assessment of the characteristics of particulate pollutants,a strategic selection of ionizer technologies,and adherence to the recommended optimal concentration thresholds of NAIs.
基金supported by the National Natural Science Foundation of China(No.62071365)the Key Research and Development Program of Shaanxi Province(No.2017ZDCXL-GY-06-02).
文摘To improve the quality of the illumination distribution,one novel indoor visible light communication(VLC)system,which is jointly assisted by the angle-diversity transceivers and simultaneous transmission and reflection-intelligent reflecting surface(STAR-IRS),has been proposed in this work.A Harris Hawks optimizer algorithm(HHOA)-based two-stage alternating iteration algorithm(TSAIA)is presented to jointly optimize the magnitude and uniformity of the received optical power.Besides,to demonstrate the superiority of the proposed strategy,several benchmark schemes are simulated and compared.Results showed that compared to other optimization strategies,the TSAIA scheme is more capable of balancing the average value and variance of the received optical power,when the maximal ratio combining(MRC)strategy is adopted at the receiver.Moreover,as the number of the STAR-IRS elements increases,the optical power variance of the system optimized by TSAIA scheme would become smaller while the average optical power would get larger.This study will benefit the design of received optical power distribution for indoor VLC systems.
