Theory of Quantum Dissipation in a Class of Non-Gaussian Environments

In this work we construct a novel dissipaton-equation-of-motion （DEOM） theory in quadratic bath coupling environment, based on an extended algebraic statistical quasi-particle approach. To validate the new ingredient of the underlying dissipaton algebra, we derive an extended Zusman equation via a totally different approach. We prove that the new theory, if it starts with the identical setup, constitutes the dynamical resolutions to the extended Zusman equation. Thus, we verify the generalized （non-Gaussian） Wick＇s theorem with dissipatons-pair added. This new algebraic ingredient enables the dissipaton approach being naturally extended to nonlinear coupling environments. Moreover, it is noticed that, unlike the linear bath coupling case, the influence of a non-Gaussian environment cannot be completely characterized with the linear response theory. The new theory has to take this fact into account. The developed DEOM theory manifests the dynamical interplay between dissipatons and nonlinear bath coupling descriptors that will be specified. Numerical demonstrations will be given with the optical line shapes in quadratic coupling environment.

Estimation Performance for the Cubature Particle Filter under Nonlinear/Non-Gaussian Environments

This paper evaluates the state estimation performance for processing nonlinear/non-Gaussian systems using the cubature particle lter(CPF),which is an estimation algorithm that combines the cubature Kalman lter(CKF)and the particle lter(PF).The CPF is essentially a realization of PF where the third-degree cubature rule based on numerical integration method is adopted to approximate the proposal distribution.It is benecial where the CKF is used to generate the importance density function in the PF framework for effectively resolving the nonlinear/non-Gaussian problems.Based on the spherical-radial transformation to generate an even number of equally weighted cubature points,the CKF uses cubature points with the same weights through the spherical-radial integration rule and employs an analytical probability density function(pdf)to capture the mean and covariance of the posterior distribution using the total probability theorem and subsequently uses the measurement to update with Bayes’rule.It is capable of acquiring a maximum a posteriori probability estimate of the nonlinear system,and thus the importance density function can be used to approximate the true posterior density distribution.In Bayesian ltering,the nonlinear lter performs well when all conditional densities are assumed Gaussian.When applied to the nonlinear/non-Gaussian distribution systems,the CPF algorithm can remarkably improve the estimation accuracy as compared to the other particle lterbased approaches,such as the extended particle lter(EPF),and unscented particle lter(UPF),and also the Kalman lter(KF)-type approaches,such as the extended Kalman lter(EKF),unscented Kalman lter(UKF)and CKF.Two illustrative examples are presented showing that the CPF achieves better performance as compared to the other approaches.

Impact of typical environments in China’s western mountainous areas on the durability of railway concrete:a review

Purpose–This study aims to analyze the impact mechanism of typical environments in China’s western mountainous areas on the durability of railway concrete and propose measures to improve durability.Design/methodology/approach–With the continuous promotion of infrastructure construction,the focus of China’s railway construction has gradually shifted to the western region.The four typical environments of large temperature differences,strong winds and dryness,high cold and low air pressure unique to the western mountainous areas of China have adverse effects on the durability of typical railway structure concrete(bridges,ballastless tracks and tunnels).This study identified the characteristics of four typical environments in the western mountainous areas of China through on-site research.The impact mechanism of the four typical environments on the durability of concrete in different structural parts of railways has been explored through theoretical analysis and experimental research;Finally,a strategy for improving the durability of railway concrete suitable for the western mountainous areas of China was proposed.Findings–The daily temperature difference in the western mountainous areas of China is more than twice that of the plain region,which will lead to significant temperature deformation and stress in the multi-layered structure of railway ballastless tracks.It will result in cracking.The wind speed in the western plateau region is about 2.5 to 3 times that of the plain region,and the average annual rainfall is only 1/5 of that in the plain region.The drying effect on the surface of casting concrete will significantly accelerate its cracking process,leading to serious durability problems.The environmental temperature in the western mountainous areas of China is generally low,and there are more freeze-thaw cycles,which will increase the risk of freeze-thaw damage to railway concrete.The environmental air pressure in the western plateau region is only 60%of that in the plain region.The moisture inside the concrete is more likely to diffuse into the surrounding environment under the pressure difference,resulting in greater water loss and shrinkage deformation of the concrete in the plateau region.The above four issues will collectively lead to the rapid deterioration of concrete durability in the western plateau region.The corresponding durability improvement suggestions from theoretical research,new technology development and standard system was proposed in this paper.Originality/value–The research can provide the mechanism of durability degradation of railway concrete in the western mountainous areas of China and corresponding improvement strategies.

Distribution and geochemical significance of alkylbenzenes for crude oil with different depositional environments and thermal maturities

A total of 45 alkylbenzenes were detected and identified in crude oils with different depositional environments and thermal maturities from the Tarim Basin,Beibuwan Basin,and Songliao Basin using comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry(GC×GCTOFMS).By analyzing the distribution characteristics of C0-C5alkylbenzenes,it is found that the content of some alkylbenzenes varies greatly in crude oils.Based on the distribution characteristics of 1,2,4,5-tetramethylbenzene(Te MB)and 1,2,3,4-Te MB,the ratio of 1,2,4,5-Te MB to 1,2,3,4-Te MB is proposed to indicate the organic matter origin and depositional environment of ancient sediments.Oil samples originated mainly from lower hydrobiont,algae,bacteria and source rocks deposited under reducing/anoxic conditions have low 1,2,4,5-/1,2,3,4-Te MB values(less than 0.6),while oil samples originated mainly from terrestrial higher plants and source rocks deposited under oxic/sub-oxic conditions have higher 1,2,4,5-/1,2,3,4-Te MB values(greater than 1.0).The significant difference of 1,2,4,5-/1,2,3,4-Te MB values is controlled by 1,2,4,5-Te MB content.1,2,4,5-Te MB content in oils derived from source rocks deposited in oxidized sedimentary environment(greater than 1.0 mg/g whole oil)is higher than that in oils from source rocks deposited in reduced sedimentary environment(less than 1.0 mg/g whole oil).1,2,4,5-/1,2,3,4-Te MB ratio might not or slightly be affected by evaporative fractionation,biodegradation and thermal maturity.1,2,4,5-/1,2,3,4-Te MB ratio and 1,2,4,5-Te MB content can be used as supplementary parameter for the identification of sedimentary environment and organic matter input.It should be noted that compared to the identification of organic matter sources,the 1,2,4,5-/1,2,3,4-Te MB parameter is more effective in identifying sedimentary environments.

Tomato detection method using domain adaptive learning for dense planting environments

This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is 90.95%,the mAP value is 91.35%,and the detection time of each image is 21 ms,which can still meet the requirements of real-time detection of tomatoes in dense planting environment.The experimental results show that the proposed TDA-YOLO model can accurately and quickly detect tomatoes in dense planting environment,and at the same time avoid the use of a large number of annotated data,which provides technical support for the development of automatic harvesting systems for tomatoes and other fruits.

Changes in human settlement environments and their drivers in valley cities located in arid and semi-arid regions:A case study of Lanzhou in Western China

Development of urban human settlement environments(HSEs)is an integral part of promoting high-quality and sustainable regional development and constructing a beautiful China.The city of Lanzhou,located at the geometric center of China,is the only provincial capital traversed by the Yellow River.Given the constraints posed by the valley topography and the need for economic development,the development of this HSE,which is located within an arid region,poses considerable challenges.Evidently,an understanding of the evolution of HSEs and drivers of changes in them contributes to high-quality,sustainable urban development in arid and semi-arid regions.An analytical model was developed using the parameters of relief degree of land surface,human comfort days,the land cover index,nighttime light index,and precipitation.This model was used in combination with population density and the gross domestic product to analyze the spatial distribution of Lanzhou's HSE and its drivers.The results showed that landscapes in Lanzhou underwent significant changes between 2000 and 2022,with an increase in building-up land(+0.946%),cultivated land(+0.134%),and forest land(+0.018%)and a decrease in grassland(-1.10%).There was significant outward expansion of the main urban zone of Lanzhou and of various county towns,with the increase in building-up land being most prominent.During this period,there were significant changes in the periphery of the core urban area and county towns in Lanzhou,with decreases moving from the urban center(the highest value)to the surrounding areas(Yongdeng County had the lowest value).The correlation between the HSE and population density grew stronger in Anning and Chengguan Districts but became weaker in Xigu and Qilihe Districts.Spatiotemporal variations in the HSE were primarily caused by climate change,followed by human activities,and were also influenced by the valley topography.Overall,the spatial distribution of population density and the HSE in Lanzhou demonstrated good consistency under the in-fluence of economic development and urbanization.

Effects of virtual agents on interaction efficiency and environmental immersion in MR environments

Background Physical entity interactions in mixed reality(MR)environments aim to harness human capabilities in manipulating physical objects,thereby enhancing virtual environment(VEs)functionality.In MR,a common strategy is to use virtual agents as substitutes for physical entities,balancing interaction efficiency with environmental immersion.However,the impact of virtual agent size and form on interaction performance remains unclear.Methods Two experiments were conducted to explore how virtual agent size and form affect interaction performance,immersion,and preference in MR environments.The first experiment assessed five virtual agent sizes(25%,50%,75%,100%,and 125%of physical size).The second experiment tested four types of frames(no frame,consistent frame,half frame,and surrounding frame)across all agent sizes.Participants,utilizing a head mounted display,performed tasks involving moving cups,typing words,and using a mouse.They completed questionnaires assessing aspects such as the virtual environment effects,interaction effects,collision concerns,and preferences.Results Results from the first experiment revealed that agents matching physical object size produced the best overall performance.The second experiment demonstrated that consistent framing notably enhances interaction accuracy and speed but reduces immersion.To balance efficiency and immersion,frameless agents matching physical object sizes were deemed optimal.Conclusions Virtual agents matching physical entity sizes enhance user experience and interaction performance.Conversely,familiar frames from 2D interfaces detrimentally affect interaction and immersion in virtual spaces.This study provides valuable insights for the future development of MR systems.

Protected simultaneous quantum remote state preparation scheme by weak and reversal measurements in noisy environments

We discuss a quantum remote state preparation protocol by which two parties, Alice and Candy, prepare a single-qubit and a two-qubit state, respectively, at the site of the receiver Bob. The single-qubit state is known to Alice while the two-qubit state which is a non-maximally entangled Bell state is known to Candy. The three parties are connected through a single entangled state which acts as a quantum channel. We first describe the protocol in the ideal case when the entangled channel under use is in a pure state. After that, we consider the effect of amplitude damping(AD) noise on the quantum channel and describe the protocol executed through the noisy channel. The decrement of the fidelity is shown to occur with the increment in the noise parameter. This is shown by numerical computation in specific examples of the states to be created. Finally, we show that it is possible to maintain the label of fidelity to some extent and hence to decrease the effect of noise by the application of weak and reversal measurements. We also present a scheme for the generation of the five-qubit entangled resource which we require as a quantum channel. The generation scheme is run on the IBMQ platform.

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.

Influences of different alkaline and acidic diagenetic environments on diagenetic evolution and reservoir quality of alkaline lake shales

Thin section and argon-ion polishing scanning electron microscope observations were used to analyze the sedimentary and diagenetic environments and main diagenesis of the Permian Fengcheng Formation shales in different depositional zones of Mahu Sag in the Junggar Basin,and to reconstruct their differential diagenetic evolutional processes.The diagenetic environment of shales in the lake-central zone kept alkaline,which mainly underwent the early stage(Ro<0.5%)dominated by the authigenesis of Na-carbonates and K-feldspar and the late stage(Ro>0.5%)dominated by the replacement of Na-carbonates by reedmergnerite.The shales from the marginal zone underwent a transition from weak alkaline to acidic diagenetic environments,with the early stage dominated by the authigenesis of Mg-bearing clay and silica and the late stage dominated by the dissolution of feldspar and carbonate minerals.The shales from the transitional zone also underwent a transition from an early alkaline diagenetic environment,evidenced by the formation of dolomite and zeolite,to a late acidic diagenetic environment,represented by the reedmergnerite replacement and silicification of feldspar and carbonate minerals.The differences in formation of authigenic minerals during early diagenetic stage determine the fracability of shales.The differences in dissolution of minerals during late diagenetic stage control the content of free shale oil.Dolomitic shale in the transitional zone and siltstone in the marginal zone have relatively high content of free shale oil and strong fracability,and are favorable“sweet spots”for shale oil exploitation and development.

A Novel Hybrid Architecture for Superior IoT Threat Detection through Real IoT Environments

As the Internet of Things(IoT)continues to expand,incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats,necessitating robust defense mechanisms.This paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world settings.Our proposed model combines Convolutional Neural Networks(CNN),Bidirectional Long Short-Term Memory(BLSTM),Gated Recurrent Units(GRU),and Attention mechanisms into a cohesive framework.This integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT systems.We evaluated our model using the RT-IoT2022 dataset,which includes various devices,standard operations,and simulated attacks.Our research’s significance lies in the comprehensive evaluation metrics,including Cohen Kappa and Matthews Correlation Coefficient(MCC),which underscore the model’s reliability and predictive quality.Our model surpassed traditional machine learning algorithms and the state-of-the-art,achieving over 99.6%precision,recall,F1-score,False Positive Rate(FPR),Detection Time,and accuracy,effectively identifying specific threats such as Message Queuing Telemetry Transport(MQTT)Publish,Denial of Service Synchronize network packet crafting tool(DOS SYN Hping),and Network Mapper Operating System Detection(NMAP OS DETECTION).The experimental analysis reveals a significant improvement over existing detection systems,significantly enhancing IoT security paradigms.Through our experimental analysis,we have demonstrated a remarkable enhancement in comparison to existing detection systems,which significantly strength-ens the security standards of IoT.Our model effectively addresses the need for advanced,dependable,and adaptable security solutions,serving as a symbol of the power of deep learning in strengthening IoT ecosystems amidst the constantly evolving cyber threat landscape.This achievement marks a significant stride towards protecting the integrity of IoT infrastructure,ensuring operational resilience,and building privacy in this groundbreaking technology.

Impact of Outdoor Activities in Different Landscape Environments on the Physical and Mental Health of College Students

To investigate whether outdoor activities have an improvement effect on the emotions of young people and the differences in the impact of outdoor activities on the emotions of young people under different landscape environmental conditions,the participating students are asked to engage in outdoor activities in different landscape environments and divided into four groups.Each group is assigned to engage in outdoor activities in four types of landscape areas:open lawn area,low shrub area,dense forest area,and waterfront area.The activities include outdoor yoga and making leaf mandalas.The results show that outdoor yoga has the best effect on reducing pulse and blood pressure and regulating emotions in open lawn area and waterfront area,followed by leaf mantras in waterfront area and outdoor yoga in dense forest area.However,both activities have no significant effect in low shrub area.

Cold environments and human metabolism: A traditional chinese medicine perspective

The concept of Traditional Chinese Medicine(TCM)emphasizes the intrinsic connection between human beings and nature,positing that the human body undergoes distinct physiological changes in response to various natural environments.Cold,as a primary external factor in cold areas,necessitates the body's autonomous adaptation to uphold optimal living conditions.The repercussions of cold on the body are both far-reaching and profound,with metabolic equilibrium adjustments playing a pivotal role.This article,rooted in the TCM principle of Yin-Yang balance,delves into the metabolic intricacies and adaptive responses to the human body in cold environments.The effects manifest in heat-producing tissues,systemic substance consumption,the blood substance concentrations,liver function,and metabolic rhythms.The article subsequently presents TCM recommendations for maintaining health in cold climates.It concludes by advocating the exploration of metabolic homeostasis changes as a key avenue for investigating the metabolic traits s of populations in cold regions.We posit that such insights will enhance comprehension of the metabolic shifts in cold region populations and advance the evolution of regional medicine.

Influence of static cartoons combined with dynamic virtual environments on preoperative anxiety of preschool-aged children undergoing surgery

BACKGROUND Preschoolers become anxious when they are about to undergo anesthesia and surgery,warranting the development of more appropriate and effective interventions.AIM To explore the effect of static cartoons combined with dynamic virtual environments on preoperative anxiety and anesthesia induction compliance in preschool-aged children undergoing surgery.METHODS One hundred and sixteen preschool-aged children were selected and assigned to the drug(n=37),intervention(n=40),and control(n=39)groups.All the children received routine preoperative checkups and nursing before being transferred to the preoperative preparation room on the day of the operation.The drug group received 0.5 mg/kg midazolam and the intervention group treatment consisting of static cartoons combined with dynamic virtual environments.The control group received no intervention.The modified Yale Preoperative Anxiety Scale was used to evaluate the children’s anxiety level on the day before surgery(T0),before leaving the preoperative preparation room(T1),when entering the operating room(T2),and at anesthesia induction(T3).Compliance during anesthesia induction(T3)was evaluated using the Induction Compliance Checklist(ICC).Changes in mean arterial pressure(MAP),heart rate(HR),and respiratory rate(RR)were also recorded at each time point.RESULTS The anxiety scores of the three groups increased variously at T1 and T2.At T3,both the drug and intervention groups had similar anxiety scores,both of which were lower than those in the control group.At T1 and T2,MAP,HR,and RR of the three groups increased.The drug and control groups had significantly higher MAP and RR than the intervention group at T2.At T3,the MAP,HR,and RR of the drug group decreased and were significantly lower than those in the control group but were comparable to those in the intervention group.Both the drug and intervention groups had similar ICC scores and duration of anesthesia induction(T3),both of which were higher than those of the control group.CONCLUSION Combining static cartoons with dynamic virtual environments as effective as medication,specifically midazolam,in reducing preoperative anxiety and fear in preschool-aged children.This approach also improve their compliance during anesthesia induction and helped maintain their stable vital signs.

Impact of coupling coordination development of port and city environments on urban economic competitiveness:Evidence from the coastal port cities in eastern China

Promoting the coupling coordination development of port and its hinterland city environments is an important way to improve urban economic competitiveness.Based on relevant data of 13 coastal port cities in eastern China from 2000 to 2018,this study explores the coupling coordination development of port and city environments and its impact on urban economic competitiveness by constructing the coupling coordination degree model and the panel threshold model.The research results show that:(1)In terms of the coupling coordination development of port and city environments,most coastal ports and their hinterland cities are in a state of moderate or serious disorder.Overall,the degree of coupling coordination of port and city environments needs to be further improved;(2)The coupling coordination degree of port and city environments has a significant impact on urban economic competitiveness,and this effect gradually increases with the development of the ports and the urban economy.Among the variables that impact the urban economic competitiveness,fixed assets investment and foreign trade are significant factors that can enhance urban economic competitiveness.(3)At present,there is a“U-shaped”relationship between the coupling coordination degree of port-city environments and the urban economic competitiveness.This relationship lies on the right side of the inflection point of the“U-shaped”curve.Therefore,following the concept of assigning priority to ecological development,expanding fixed assets investment and actively developing foreign trade can further enhance the urban economic competitiveness.

Intrusion Detection System for Smart Industrial Environments with Ensemble Feature Selection and Deep Convolutional Neural Networks

Smart Industrial environments use the Industrial Internet of Things(IIoT)for their routine operations and transform their industrial operations with intelligent and driven approaches.However,IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet.Traditional signature-based IDS are effective in detecting known attacks,but they are unable to detect unknown emerging attacks.Therefore,there is the need for an IDS which can learn from data and detect new threats.Ensemble Machine Learning(ML)and individual Deep Learning(DL)based IDS have been developed,and these individual models achieved low accuracy;however,their performance can be improved with the ensemble stacking technique.In this paper,we have proposed a Deep Stacked Neural Network(DSNN)based IDS,which consists of two stacked Convolutional Neural Network(CNN)models as base learners and Extreme Gradient Boosting(XGB)as the meta learner.The proposed DSNN model was trained and evaluated with the next-generation dataset,TON_IoT.Several pre-processing techniques were applied to prepare a dataset for the model,including ensemble feature selection and the SMOTE technique.Accuracy,precision,recall,F1-score,and false positive rates were used to evaluate the performance of the proposed ensemble model.Our experimental results showed that the accuracy for binary classification is 99.61%,which is better than in the baseline individual DL and ML models.In addition,the model proposed for IDS has been compared with similar models.The proposed DSNN achieved better performance metrics than the other models.The proposed DSNN model will be used to develop enhanced IDS for threat mitigation in smart industrial environments.

A Study on the Effects of Different Interaction Combinations and Language Levels on Continuous Writing in Online Environments

This study was conducted with non-English sophomore students,aiming to explore the effects of different interaction combinations and language levels on continuous writing in an online environment,and compare the differences in lexical alignments and composition quality of learners with different interaction combinations and language levels in the same continuous writing task through experiments.The results show that the mean values of the word-phrase alignment of the paired group were higher than those of the individual group in different interaction combinations,and the two groups showed significant differences;in terms of composition quality,the individual group was better than the paired group,but there was no significant difference between the two groups in terms of task continuation.Secondly,the word-phrase alignment and composition scores of the different language-level groups were higher than those of the same language-level groups,and there was a significant difference between the two groups in terms of word-phrase alignments,but not in terms of composition scores.The results of this study can be useful and informative for second language teachers in future continuous teaching in online environments.

Study on Durability of Recycled Aggregate Concrete in High-Temperature and Complex Environments

Recycled aggregate concrete refers to a new type of concrete material made by processing waste concrete materials through grading,crushing,and cleaning,and then mixing them with cement,water,and other materials in a certain gradation or proportion.This type of concrete is highly suitable for modern construction waste disposal and reuse and has been widely used in various construction projects.It can also be used as an environmentally friendly permeable brick material to promote the development of modern green buildings.However,practical applications have found that compared to ordinary concrete,the durability of this type of concrete is more susceptible to high-temperature and complex environments.Based on this,this paper conducts theoretical research on its durability in high-temperature and complex environments,including the current research status,existing problems,and application prospects of recycled aggregate concrete’s durability in such environments.It is hoped that this analysis can provide some reference for studying the influence of high-temperature and complex environments on recycled aggregate concrete and its subsequent application strategies.

Will high-entropy carbides and borides be enabling materials for extreme environments?

The concept of multi-principal component has created promising opportunities for the development of novel high-entropy ceramics for extreme environments encountered in advanced turbine engines, nuclear reactors, and hypersonic vehicles, as it expands the compositional space of ceramic materials with tailored properties within a single-phase solid solution. The unique physical properties of some high-entropy carbides and borides, such as higher hardness, high-temperature strength, lower thermal conductivity, and improved irradiation resistance than the constitute ceramics, have been observed. These promising properties may be attributed to the compositional complexity, atomic-level disorder, lattice distortion, and other fundamental processes related to defect formation and phonon scattering.This manuscript serves as a critical review of the recent progress in high-entropy carbides and borides, focusing on synthesis and evaluations of their performance in extreme high-temperature, irradiation, and gaseous environments.

Development of a building information model-guided post-earthquake building inspection framework using 3D synthetic environments

Computer vision-based inspection methods show promise for automating post-earthquake building inspections.These methods survey a building with unmanned aerial vehicles and automatically detect damage in the collected images.Nevertheless,assessing the damage′s impact on structural safety requires localizing damage to specific building components with known design and function.This paper proposes a BIM-based automated inspection framework to provide context for visual surveys.A deep learning-based semantic segmentation algorithm is trained to automatically identify damage in images.The BIM automatically associates any identified damage with specific building components.Then,components are classified into damage states consistent with component fragility models for integration with a structural analysis.To demonstrate the framework,methods are developed to photorealistically simulate severe structural damage in a synthetic computer graphics environment.A graphics model of a real building in Urbana,Illinois,is generated to test the framework;the model is integrated with a structural analysis to apply earthquake damage in a physically realistic manner.A simulated UAV survey is flown of the graphics model and the framework is applied.The method achieves high accuracy in assigning damage states to visible structural components.This assignment enables integration with a performance-based earthquake assessment to classify building safety.