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We propose a novel reception scheme for twin single-sideband (twin-SSB) signals using just a single photodetector (PD), significantly reducing the system complexity and cost. To detect a twin-SSB with power-unbalanced quadrature-phase shift keying (QPSK) sidebands upon detection via a single PD at the receiver side, two QPSKs carried in two sidebands are coherently superposed and detected in a 16-ary quadrature amplitude modulation (16-QAM) format. This technique notably diminishes the linearity and effective number of bits required for the transmitter components in high-speed optical transmission systems. Moreover, a hierarchical blind-phase search (HBPS) algorithm is proposed to compensate for the imperfect phase rotation of QPSK signals during transmission. To demonstrate the effectiveness of our proposed method, we successfully conducted simulations of 112 Gb/s 16-QAM signal transmission over a 10 km standard single-mode fiber (SSMF), achieving bit error ratios (BERs) of 7.84×10−4, well below the 7% hard-decision forward error correction (HD-FEC) threshold of 3.8×10−3. In addition, the synthetic transmission scheme proposed in this paper is compared with the traditional 16-QAM signal transmission scheme, and the results show that the proposed scheme does not introduce a performance cost with the same received optical power (ROP) and transmission distance.

The lower valence compensation of YMn1-xCuxO3 (x = 0.00, 0.05, and 0.10) is prepared by the solid-state reaction, and the effects of divalent cation Cu-doping on the construction and magnetic and dielectric attributes of multiferroic YMnO3 are systemically researched. Powder X-ray diffraction shows YMn1-xCuxO3 has a single-phase hexagonal construction with a P63cm space group as the parent YMnO3, and lattice parameters decrease systematically as Cu concentration increases. Using the scanning electric microscope, structure morphologies analysis shows that the mean grain size varies between 1.90 and 2.20 μm as Cu content increases. YMn1-xCuxO3 magnetization increases as Cu doping concentration increases, and the antiferromagnetic transition temperature declines from 71 K for x = 0.00 to 58 K for x = 0.10. The valence distributions of Mn ions conduce to the modified magnetic attributes. Due to Cu substitution, the dielectric loss and dielectric constant decline as frequency increases from 400 to 700 K, showing representative relaxation behaviors. Indeed, that is a thermally activated process. In addition, the peak of the dielectric loss complies with the Arrhenius law. The relaxation correlates to the dipole effect regarding carrier hopping between Mn3+ and Mn4+, and also correlates to oxygen vacancies generated by Mn2+.

Human alpha oscillation (7–13 Hz) has been extensively studied over the years for its connection with cognition. The individual alpha frequency (IAF), defined as the frequency that provides the highest power in the alpha band, shows a positive correlation with cognitive processes. The modulation of alpha activities has been accomplished through various approaches aimed at improving cognitive performance. However, very few studies focused on the direct modulation of IAF by shifting the peak frequency, and the understanding of IAF modulation remains highly limited. In this study, IAFs of healthy young adults were up-regulated through short-term neurofeedback training using haptic feedback. The results suggest that IAFs have good trainability and are up-regulated, also that IAFs are correlated with the enhanced cognitive performance in mental rotation and n-back tests compared to sham-neurofeedback control. This study demonstrates the feasibility of self-regulating IAF for cognition enhancement and provides potential therapeutic benefits for cognitive-impaired patients.

The buckling enhancement of the negative Poisson’s ratio (NPR) effect on a laminated plate under uniaxial compression with an in-plane translational restraint is investigated in this paper. According to the buckling equation of an orthotropic plate under biaxial compression, the critical buckling load of an NPR-laminated composite under uniaxial compression can be increased due to the induced tension force on the unloaded direction. Instead of layer angles and stacking sequence, the NPR envelope and buckling load enhancement are studied using lamination parameters in this paper. The Poisson’s ratio contours are given in the feasible region of membrane lamination parameters. The results show that the negative Poisson’s ratios are more sensitive to V3A, which represents the unbalance degree of the laminate. Furthermore, the buckling loads for various Poisson’s ratio layups are investigated, and it is concluded that the buckling load increases with a decrease in Poisson’s ratio for the laminated rectangular plate considering in-plane translational restraint. Finally, the inverse problem of deciding the laminate configuration to target the lamination parameters is solved using the particle swarm optimization (PSO) algorithm.

Traffic flow prediction is one of the most important tasks of the Intelligent Transportation Systems (ITSs) for traffic management, and it is also a challenging task affected by many complex factors, such as weather and time. Many cities adopt efficient traffic prediction methods to control traffic congestion. However, most of the existing methods of traffic prediction focus on urban road scenarios, neglecting the complexity of multivariate auxiliary information in highways. Moreover, these methods have difficulty explaining the prediction results based only on the historical traffic flow sequence. To tackle these problems, we propose a novel traffic prediction model, namely Multi-variate and Multi-horizon prediction based on Long Short-Term Memory (MMLSTM). MMLSTM can effectively incorporate auxiliary information, such as weather and time, based on a strategy of multi-horizon time spans to improve the prediction performance. Specifically, we first exploit a multi-horizon bidirectional LSTM model for fusing the multivariate auxiliary information in different time spans. Then, we combine an attention mechanism and multi-layer perceptron to conduct the traffic prediction. Furthermore, we can use the information of multivariate (weather and time) to provide interpretability to manage the model. Comprehensive experiments are conducted on Hangst and Metr-la datasets, and MMLSTM achieves better performance than baselines on traffic prediction tasks.

SiC fiber-reinforced AlPO4 matrix (SiCf/MWCNTs/AlPO4) composites were fabricated using a hot laminating process with multi-walled carbon nanotubes (MWCNTs) as the absorber. A coating prepared from SiO2 + SiC + Al (H2PO4)3 was applied to the surface of the SiCf/MWCNTs/AlPO4 composites prior to an anti-oxidation test at 1273 K in air for 40 h. The anti-oxidation effect was verified by a three-point bending test, scanning electron microscopy, transmission electron microscopy, X-ray diffraction, and a dielectric property test. Anti-oxidation mechanism investigations revealed that the coating effectiveness could be attributed to three substances, i.e., SiO2, SiP2O7, and SiO2 + AlPO4 solid solution from the reactions of SiC + O2→SiO2 + CO, SiO2 + P2O5→SiP2O7 and SiO2 + AlPO4→solid solution, respectively.

Detecting the characteristic decomposition products (SO2, SOF2, and HF) of SF6 is an effective way to diagnose the electric discharge in SF6-insulated equipment. Based on first-principles calculations, Au, Ag, and Cu were chosen as the surface modification transition metal to improve the adsorption and gas-sensing properties of MoTe2 monolayer towards SO2, SOF2, and HF gases. The results show that Au, Ag, and Cu atoms tend to be trapped by TH sites on the MoTe2 monolayer, and the binding strength increases in the order of Ag < Au < Cu. In gas adsorption, the moderate adsorption energy provides the basis that the TM-MoTe2 monolayer can be used as gas-sensing material for SO2, SOF2, and HF. The conductivity of the adsorption system changes significantly. The conductivity decreases upon gases adsorption on TM-MoTe2 monolayer, except the conductivity of Ag-MoTe2 monolayer increases after interacting with SOF2 gas.

The generation of high field terahertz emission based on the interaction between an ultra-intense laser and solid targets has been widely studied in recent years because of its wide potential applications in biological imaging and material science. Here, a novel scheme is proposed to enhance the terahertz emission, in which a linearly polarized laser pulse irradiates a T-type target including a longitudinal target followed by a transverse target. By using two-dimensional particle-in-cell simulations, we find that the electron beam, modulated by the direct laser acceleration via the interaction of the laser with the longitudinal solid target, plays a crucial role in enhancing the intensity of terahertz emission and controlling its spatial distribution. Compared with the single-layer target, the maximum radiated electromagnetic field’s intensity passing through the spatial probe point is enhanced by about one order of magnitude, corresponding to the terahertz emission power increasing by two orders of magnitude or so. In addition, the proposed scheme is robust with respect to the thickness and length of the target. Such a scheme may provide important theoretical and data support for the enhancement of terahertz emission efficiency based on the ultra-intense laser irradiation of solid targets.

The microstructures and mechanical properties of (Cu0.5Zr0.5)100−xZnx (x = 0, 1.5, 2.5, 4.5, 7, 10, and 14 at. %) bulk metallic glass (BMG) composites were studied. CuZr martensitic crystals together with minor B2 CuZr and amorphous phases dominate the microstructures of the as-quenched samples with low Zn additions (x = 0, 1.5, and 2.5 at. %), while B2 CuZr and amorphous phases being accompanied with minor martensitic crystals form at a higher Zn content (x = 4.5, 7, 10, and 14 at. %). The fabricated Cu-Zr-Zn BMG composites exhibit macroscopically appreciable compressive plastic strain and obvious work-hardening due to the formation of multiple shear bands and the deformation-induced martensitic transformation (MT) within B2 crystals. The present BMG composites could be a good candidate as high-performance structural materials.

The herb and fruits of Leonurus japonicus Houtt., named “Yimucao” and “Chongweizi”, respectively, in Chinese, have been widely used in China as gynecological medicines. The components of the essential oils obtained by hydrodistillation were investigated by GC-MS. The antibacterial activity of the essential oils was determined by micro-dilution assay. The results showed large variations in the chemical composition and antibacterial activity of the oils. The oil of “Yimucao” showed antibacterial activity against various Gram-positive bacteria and consisted mainly of sesquiterpenes and diterpenes, with phytone, phytol, caryophyllene oxide and β-caryophyllene being the most significant constituents, whereas the oil of “Chongweizi”, mainly made up of bornyl acetate and aliphatic hydrocarbons, was inactive in the antibacterial assay. Further study of the main compounds in “Yimucao oil” showed that β-caryophyllene had wide-spectrum activity against Gram-positive bacteria.

Two new alkaloids, aconicarmine (1) and aconicaramide (5), were isolated from the EtOH extract of the lateral roots of Aconitum carmichaelii, together with five known compounds: fuziline (2), neoline (3), N-ethylhokbusine B (4), 5-hydroxymethylpyrrole-2-carbaldehyde (6), and oleracein E (7). Their structures were elucidated by physical and NMR analysis. Pyrrole alkaloids were isolated from A. carmichaelii for the first time. In the in vitro assays, compounds 2 and 3 showed activity against pentobarbital sodium-induced cardiomyocytes damage by obviously recovering beating rhythm and increasing the cell viability, while compounds 5 and 7 showed moderate antibacterial activity.

In today’s interconnected world, the need for robust intrusion and malware detection and prevention has never been more critical [...]

In this study, we investigated the protective effects of astaxanthin (AST) against oxidative stress induced by the combination of azoxymethane (AOM) and dextran sulfate sodium (DSS) in colitis-associated cancer (CAC) and TNF-α-induced human colorectal cancer cells (SW480), as well as the underlying mechanism. In vitro experiments revealed that astaxanthin reduced reactive oxygen species (ROS) generation and inhibited the expression of Phosphorylated JNK (P-JNK), Phosphorylated ERK (P-ERK), Phosphorylated p65 (P-p65), and the NF-κB downstream protein cyclooxygenase-2 (COX-2). In vivo experiments showed that astaxanthin ameliorated AOM/DSS-induced weight loss, shortened the colon length, and caused histomorphological changes. In addition, astaxanthin suppressed cellular inflammation by modulating the MAPK and NF-κB pathways and inhibiting the expression of the proinflammatory cytokines IL-6, IL-1β, and TNF-α. In conclusion, astaxanthin attenuates cellular inflammation and CAC through its antioxidant effects.

Mooring chains are key components of offshore floating photovoltaic systems. Although their service safety is often affected by the harsh service environment, the influence of surface roughness on their corrosion resistance is not clear. This study investigated the corrosion behavior of mooring chain steel using cyclic salt-spray corrosion and electrochemical tests. Scanning electron microscopy, energy-dispersive spectrometry, optical profilometry, and other analytical techniques were used to study the composition and morphology of the corrosion products. The corrosion behavior was studied by electrochemical polarization curves, alternating current impedance spectroscopy, and X-ray photoelectron spectroscopy. The results show that the salt-spray corrosion resistance of mooring chain steel significantly improved with the reduction in specimen surface roughness, and the number and depth of corrosion pits were reduced. Mass loss after 24 h of salt-spray corrosion was exponentially related to initial roughness (Ra). Improved surface roughness significantly increased the pitting potential of the specimens, widened the passivation range, and enhanced the repassivation capability, thus significantly improving the pitting resistance. The pitting potential is linearly related to the initial roughness of the specimen. The oxide contents of Fe, Mo, and Si in the passivation film tended to increase with a smoother surface, which contributes to its densification. This effectively blocks chloride ion attack, thus improving the corrosion resistance of the mooring chain steel.

Despite the swift advancement of geometric calibration techniques, the geometric performance of remote sensing imagery remains heavily contingent upon the quality and distribution of ground control data. Securing precise ground control data is often laborious, and the accuracy of open-source control data is subject to variability. This paper explores the potential of the globally dispersed International GNSS Service (IGS) network to enhance the geometric performance of remote sensing images. The IGS network, with its extensive reach, offers superior positioning and navigation products that surpass the previously mentioned sources. To establish a connection between the IGS network and remote sensing images, high-resolution GEM chips (GEMs) are firstly utilized for precise positioning. Geolocation biases of these GEMs are refined based on the identified IGS information. After that, the calibrated GEM chips are applied as control information for the geometric calibration of raw satellite images. A test dataset from the Chinese Gaofen-2 (GF-2) with various forms of coverage is experimented, with LiDAR-derived Digital Surface Models (DSMs) serving as reference for the validation of the proposed method. Compared with traditional methods using the GEMs as a direct reference, the experimental results demonstrate that the introduced IGS information enhances the geometric performance of remote sensing images, exhibiting robust generalization performance across remote sensing data from various source domains.

Surface ozone (O3) pollution has adverse impacts on the yield of winter wheat. The North China Plain (NCP), one of the globally significant primary regions for winter wheat production, has been frequently plagued by severe O3 pollution in recent years. In this study, the effects of O3 pollution on winter wheat yield and economic impact were evaluated in the NCP during the 2015–2018 seasons using the regional atmospheric chemical transport model (WRF-Chem), O3 metrics including the phytotoxic surface O3 dose above 12 nmol m−2 s−1 (POD12), and the accumulated daytime O3 above 40 ppb (AOT40). Results showed that the modeled O3, exposure-based AOT40, and flux-based POD12 increased during the winter wheat growing season from 2015 to 2018. The annual average daytime O3, exposure-based AOT40, and flux-based POD12 were 44 ppb, 5.32 ppm h, and 1.78 mmol m−2, respectively. During 2015–2018, winter wheat relative production loss averaged 10.9% with AOT40 and 14.6% with POD12. This resulted in an average annual production loss of 12.4 million metric tons, valued at approximately USD 4.5 billion. This study enhances our understanding of the spatial sensitivity of winter wheat to O3 impacts, and suggests that controlling O3 pollution during the key growth stages of winter wheat or improving its O3 tolerance will enhance food security.

The addition of flexible Cu2ZnSnS4 (CZTS) thin film solar cells to titanium (Ti) substrates is an attractive way to achieve the low-cost manufacturing of photovoltaics. Prior research has indicated that the appropriate diffusion of Ti elements can enhance the crystalline growth of CZTS films. However, the excessive diffusion of Ti has been shown to adversely affect the photovoltaic performance of CZTS photovoltaic devices. Therefore, it is essential to regulate the diffusion of Ti elements within CZTS thin films to optimize their photovoltaic properties. The tendency for Ti substrate elements to diffuse into CZTS films is also influenced by the activation energy associated with these Ti elements. The sulfurization temperature is posited to be a critical factor in modulating the diffusion and activation energy of Ti elements within CZTS thin films. Consequently, this research investigates the alteration of the sulfurization temperature of CZTS thin films in order to enhance the properties of these thin films and to examine the diffusion behavior of titanium elements. The results reveal that as the sulfurization temperature increases, the diffusion of Ti elements within the CZTS thin films initially increases, then decreases, and subsequently increases again. This pattern suggests that the diffusion of Ti elements is affected not only by the activation energy of the Ti elements but also by the defect hopping distance within the CZTS thin films. Notably, at a sulfurization temperature of 550 °C, the grains at the base of the CZTS thin film demonstrate an increased density, which is associated with a reduced defect hopping distance, thereby hindering the diffusion of Ti elements within the CZTS thin films. Furthermore, at this specific sulfurization temperature, the slope of the current–voltage (I–V) curve for the CZTS/Ti structure reaches its maximum, indicating optimal ohmic contact characteristics.

Background and aims: We previously reported that Oxy210, an oxysterol-based drug candidate, exhibits antifibrotic and anti-inflammatory properties. We also showed that, in mice, it ameliorates hepatic hallmarks of non-alcoholic steatohepatitis (NASH), including inflammation and fibrosis, and reduces adipose tissue inflammation. Here, we aim to investigate the effects of Oxy210 on atherosclerosis, an inflammatory disease of the large arteries that is linked to NASH in epidemiologic studies, shares many of the same risk factors, and is the major cause of mortality in people with NASH. Methods: Oxy210 was studied in vivo in APOE*3-Leiden.CETP mice, a humanized mouse model for both NASH and atherosclerosis, in which symptoms are induced by consumption of a high fat, high cholesterol “Western” diet (WD). Oxy210 was also studied in vitro using two cell types that are important in atherogenesis: human aortic endothelial cells (HAECs) and macrophages treated with atherogenic and inflammatory agents. Results: Oxy210 reduced atherosclerotic lesion formation by more than 50% in hyperlipidemic mice fed the WD for 16 weeks. This was accompanied by reduced plasma cholesterol levels and reduced macrophages in lesions. In HAECs and macrophages, Oxy210 reduced the expression of key inflammatory markers associated with atherosclerosis, including interleukin-1 beta (IL-1β), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), chemokine (C-C motif) ligand 2 (CCL2), vascular cell adhesion molecule-1 (VCAM-1), and E-Selectin. In addition, cholesterol efflux was significantly enhanced in macrophages treated with Oxy210. Conclusions: These findings suggest that Oxy210 could be a drug candidate for targeting both NASH and atherosclerosis, as well as chronic inflammation associated with the manifestations of metabolic syndrome.

Orbit error is one of the primary error sources of interferometric synthetic aperture radar (InSAR) and differential InSAR (D-InSAR) measurements, arising from inaccurate orbit determination of SAR platforms. Typically, orbit error in the interferogram can be estimated using polynomial models. However, correcting for orbit errors with significant time-varying characteristics presents two main challenges: (1) the complexity and variability of the azimuth time-varying orbit errors make it difficult to accurately model them using a set of polynomial coefficients; (2) existing patch-based polynomial models rely on empirical segmentation and overlook the time-varying characteristics, resulting in residual orbital error phase. To overcome these problems, this study proposes an automated block adjustment framework for estimating time-varying orbit errors, incorporating the following innovations: (1) the differential interferogram is divided into several blocks along the azimuth direction to model orbit error separately; (2) automated segmentation is achieved by extracting morphological features (i.e., peaks and troughs) from the azimuthal profile; (3) a block adjustment method combining control points and connection points is proposed to determine the model coefficients of each block for the orbital error phase estimation. The feasibility of the proposed method was verified by repeat-pass L-band spaceborne and P-band airborne InSAR data, and finally, the InSAR digital elevation model (DEM) was generated for performance evaluation. Compared with the high-precision light detection and ranging (LiDAR) elevation, the root mean square error (RMSE) of InSAR DEM was reduced from 18.27 m to 7.04 m in the spaceborne dataset and from 7.83~14.97 m to 3.36~6.02 m in the airborne dataset. Then, further analysis demonstrated that the proposed method outperforms existing algorithms under single-baseline and single-polarization conditions. Moreover, the proposed method is applicable to both spaceborne and airborne InSAR data, demonstrating strong versatility and potential for broader applications.

This paper addresses image enhancement and 3D reconstruction techniques for dim scenes inside the vacuum chamber of a nuclear fusion reactor. First, an improved multi-scale Retinex low-light image enhancement algorithm with adaptive weights is designed. It can recover image detail information that is not visible in low-light environments, maintaining image clarity and contrast for easy observation. Second, according to the actual needs of target plate defect detection and 3D reconstruction inside the vacuum chamber, a defect reconstruction algorithm based on photometric stereo vision is proposed. To optimize the position of the light source, a light source illumination profile simulation system is designed in this paper to provide an optimized light array for crack detection inside vacuum chambers without the need for extensive experimental testing. Finally, a robotic platform mounted with a binocular stereo-vision camera is constructed and image enhancement and defect reconstruction experiments are performed separately. The results show that the above method can broaden the gray level of low-illumination images and improve the brightness value and contrast. The maximum depth error is less than 24.0% and the maximum width error is less than 15.3%, which achieves the goal of detecting and reconstructing the defects inside the vacuum chamber.

Multispectral remote sensing images contain abundant information about the distribution and reflectance of ground objects, playing a crucial role in target detection, environmental monitoring, and resource exploration. However, due to the complexity of the imaging process in multispectral remote sensing, image blur is inevitable, and the blur kernel is typically unknown. In recent years, many researchers have focused on blind image deblurring, but most of these methods are based on single-band images. When applied to CASEarth satellite multispectral images, the spectral correlation is unutilized. To address this limitation, this paper proposes a novel approach that leverages the characteristics of multispectral data more effectively. We introduce an inter-band gradient similarity prior and incorporate it into the patch-wise minimal pixel (PMP)-based deblurring model. This approach aims to utilize the spectral correlation across bands to improve deblurring performance. A solution algorithm is established by combining the half-quadratic splitting method with alternating minimization. Subjectively, the final experiments on CASEarth multispectral images demonstrate that the proposed method offers good visual effects while enhancing edge sharpness. Objectively, our method leads to an average improvement in point sharpness by a factor of 1.6, an increase in edge strength level by a factor of 1.17, and an enhancement in RMS contrast by a factor of 1.11.

The Nd-mediated coordinative chain transfer polymerization (CCTP) of dienes represents one of the state-of-the-art techniques in the current synthetic rubber field. Besides having well-controlled polymerization behaviors as well as high atom economies, it also allows for the generation of highly reactive Al-capped polydienyl chain-ends, which hold great potential, yet much less explored up to date, in achieving end functionalization to mimic the structure of natural rubber. In this study, we demonstrate an efficient in situ method to realize end-functionalizing polyisoprene by introducing epoxide compounds into a CCTP system. The end functionalization efficiency was 92.7%, and the obtained polymers were systematically characterized by 1H NMR, 1H,1H-COSY NMR, DOSY NMR, and MALDI TOF. NMR studies revealed that a maximum of two EO units were introduced to the chain ends, and based on density functional theory (DFT) studies, an energy barrier of 33.3 kcal/mol was required to be overcome to open the ring of the EO monomer. Increasing the ratio of [Ip]/[Nd] resulted in gradually increased viscosities for the reaction medium and therefore gave rise to an end functionalization efficiency that decreased from 92.7% to 74.2%. The end hydroxyl group can also be readily converted to other functionalities, as confirmed by NMR spectroscopy.

Trigger–action programming (TAP) enables users to automate Internet of Things (IoT) devices by creating rules such as “IF Device1.TriggerState is triggered, THEN Device2.ActionState is executed”. As the number of IoT devices grows, the combination space between the functions provided by devices expands, making manual rule creation time-consuming for end-users. Existing TAP recommendation systems enhance the efficiency of rule discovery but face two primary issues: they ignore the association of rules between users and fail to model collaborative information among users. To address these issues, this article proposes a graph contrastive learning-based recommendation system for TAP rules, named GCL4TAP. In GCL4TAP, we first devise a data partitioning method called DATA2DIV, which establishes cross-user rule relationships and is represented by a user–rule bipartite graph. Then, we design a user–user graph to model the similarities among users based on the categories and quantities of devices that they own. Finally, these graphs are converted into low-dimensional vector representations of users and rules using graph contrastive learning techniques. Extensive experiments conducted on a real-world smart home dataset demonstrate the superior performance of GCL4TAP compared to other state-of-the-art methods.

Taste and odor (T&O) compounds have emerged as crucial parameters for assessing water quality. Therefore, identifying effective methodologies for the removal of these compounds is imperative. In this study, an effective approach utilizing laccase assisted by a micro-electric field was developed for the degradation of 2-methylisoborneol (2-MIB). For this purpose, the optimal conditions for the laccase-catalyzed degradation of 2-MIB were determined, and they were pH 4.0, 25 °C, 150 rpm, 0.1 U/mL of laccase, and 200 ng/L of 2-MIB. Under these specified conditions, the degradation efficiency of 2-MIB was approximately 78% after a 4 h reaction period. Subsequently, the introduction of an electric field yielded a synergistic effect with the enzyme for 2-MIB degradation. At an electric current intensity of 0.04 A over a 4 h duration, the degradation efficiency increased to 90.78%. An analysis using SPME-GC/MS provided information on the degradation intermediates of 2-MIB resulting from laccase-catalyzed degradation, electrocatalytic degradation, and micro-electric-assisted laccase degradation. The potential degradation pathways of 2-MIB illustrated that these three methods result in common degradation products, such as capric aldehyde, nonylaldehyde, and 2-ethylhexanol, and their final products include 3-pentanone, acetone, and 2-butanone. This study provides an enzyme–electrochemical method for the efficient and rapid degradation and removal of 2-MIB. The strategy of laccase catalysis assisted by a micro-electric field has good potential for the removal of pollutants from the natural environment.

Ecosystems that experience elevated CO2 (eCO2) are crucial interfaces where intricate interactions between plants and microbes occur. This study addressed the impact of eCO2 and a N supply on faba bean (Vicia faba L.) growth and the soil microbial community in auto-controlled growth chambers. In doing so, two ambient CO2 concentrations (aCO2, daytime/nighttime = 410/460 ppm; eCO2, 550/610 ppm) and two N supplement levels (without a N supply—N0—and 100 mg N as urea per kg of soil—N100) were applied. The results indicated that eCO2 mitigated the inhibitory effects of a N deficiency on legume photosynthesis and affected the CO2 assimilation efficiency, in addition to causing reduced nodulation. While the N addition counteracted the reductions in the N concentrations across the faba beans’ aboveground and belowground plant tissues under eCO2, the CO2 concentrations did not significantly alter the soil NH4+-N or NO3−-N responses to a N supply. Notably, under both aCO2 and eCO2, a N supply significantly increased the relative abundance of Nitrososphaeraceae and Nitrosomonadaceae, while eCO2 specifically reduced the Rhizobiaceae abundance with no significant changes under aCO2. A redundancy analysis (RDA) highlighted that the soil pH (p < 0.01) had the most important influence on the soil microbial community. Co-occurrence networks indicated that the eCO2 conditions mitigated the impact of a N supply on the reduced structural complexity of the soil microbial communities. These findings suggest that a combination of eCO2 and a N supply to crops can provide potential benefits for managing future climate change impacts on crop production.

This study leveraged 2019 online data of particulate matter (PM2.5) and volatile organic compounds (VOCs) in Tianjin to analyze atmospheric pollution characteristics. PM2.5 was found to be primarily composed of water-soluble ions, with nitrates as the dominant component, while VOCs were predominantly alkanes, followed by alkenes and aromatic hydrocarbons, with notable concentrations of propane, ethane, ethylene, toluene, and benzene. The receptor model identified six major sources of PM2.5 and seven major sources of VOCs. The secondary source is the main contribution source, while motor vehicles and coal burning are important primary contribution sources in PM2.5. And, industrial processes and natural gas volatilization were considered major contributors for VOCs. A health risk assessment indicated negligible non-carcinogenic risks but potential carcinogenic risks from trace metals As and Cr, and benzene within VOCs, underscoring the necessity for focused public health measures. A risk attribution analysis attributed As and Cr in PM to coal combustion and vehicular emissions. Benzene in VOCs primarily originates from fuel evaporation, and industrial and vehicular emissions. These findings underscore the potential for reducing health risks from PM and VOCs through enhanced regulation of emissions in coal, industry, and transportation. Such strategies are vital for advancing air quality management and safeguarding public health.

Elevated CO2 levels (eCO2) pose challenges to wheat (Triticum aestivum L.) growth, potentially leading to a decline in quality and productivity. This study addresses the effects of two ambient CO2 concentrations (aCO2, daytime/nighttime = 410/450 ± 30 ppm and eCO2, 550/600 ± 30 ppm) and two nitrogen (N) supplements (without N supply—N0 and with 100 mg N supply as urea per kg soil—N100) on wheat (T. aestivum cv. Yunmai) growth, N accumulation, and soil microbial communities related to ammonia oxidization. The data showed that the N supply effectively mitigated the negative impacts of eCO2 on wheat growth by reducing intercellular CO2 concentrations while enhancing photosynthesis parameters. Notably, the N supply significantly increased N concentrations in wheat tissues and biomass production, thereby boosting N accumulation in seeds, shoots, and roots. eCO2 increased the agronomic efficiency of applied N (AEN) and the physiological efficiency of applied N (PEN) under N supply. Plant tissue N concentrations and accumulations are positively related to plant biomass production and soil NO3−-N. Additionally, the N supply increased the richness and evenness of the soil microbial community, particularly Nitrososphaeraceae, Nitrosospira, and Nitrosomonas, which responded differently to N availability under both aCO2 and eCO2. These results underscore the importance and complexity of optimizing N supply and eCO2 for enhancing crop tissue N accumulation and yield production as well as activating nitrification-related microbial activities for soil inorganic N availability under future global environment change scenarios.

The transition to sustainable energy has ushered in the era of electrofuels (e-fuels), which are synthesised using electricity from renewable sources, water, and CO2 as a sustainable alternative to fossil fuels. This paper presents a systematic review of the techno-economic (TEA) and life cycle assessments (LCAs) of e-fuel production. We critically evaluate advancements in production technologies, economic feasibility, environmental implications, and potential societal impacts. Our findings indicate that while e-fuels offer a promising solution to reduce carbon emissions, their economic viability depends on optimising production processes and reducing input material costs. The LCA highlights the necessity of using renewable energy for hydrogen production to ensure the genuine sustainability of e-fuels. This review also identifies knowledge gaps, suggesting areas for future research and policy intervention. As the world moves toward a greener future, understanding the holistic implications of e-fuels becomes paramount. This review aims to provide a comprehensive overview to guide stakeholders in their decision-making processes.

To ensure the stable operation of a wind turbine generator system when the wind speed exceeds the rated value and address the issue of excessive rotor speed during high wind speeds, this paper proposes a novel variable pitch controller strategy based on a back-propagation neural network and optimal control theory to solve this problem. Firstly, a mathematical model for the wind turbine is established and linearized. Then, each optimal sub-controller is designed for different wind speed conditions by optimal theory. Subsequently, a back-propagation neural network is utilized to learn the variation pattern of controller parameters with respect to wind speed. Finally, real-time changes in wind speed are applied to evaluate and adjust controller parameters using the trained back-propagation neural network. The model is simulated in MATLAB 2019b, real-time data are observed, and the control effect is compared with that of a Takagi–Sugeno optimal controller, firefly algorithm optimal controller and fuzzy controller. The simulation results show that the rotor speed overshoot of the optimal controller under the step wind speed is the smallest, only 0.05 rad/s. Under other wind speed conditions, the rotor speed range fluctuates around 4.35 rad/s, and the fluctuation size is less than 0.2 rad/s, which is much smaller than the fluctuation range of other controllers. It can be seen that the back-propagation optimal controller can ensure the stability of the rotor speed above the rated wind speed. At the same time, it has better control accuracy compared to other controllers.

Revealing the response of vegetation on the Qinghai-Tibet Plateau (QTP) to climate change and human activities is crucial for ensuring East Asian ecological security and regulating the global climate. However, the current research rarely explores the time-lag effects of climate on vegetation growth, leading to considerable uncertainty in analyzing the driving mechanisms of vegetation changes. This study identified the main driving factors of vegetation greenness (vegetation index, EVI) changes after investigating the lag effects of climate. By analyzing the trends of interannual variation in vegetation and climate, the study explored the driving mechanisms behind vegetation changes on the QTP from 2000 to 2020. The results indicate that temperature and precipitation have significant time-lag effects on vegetation growth. When considering the lag effects, the explanatory power of climate on vegetation changes is significantly enhanced for 29% of the vegetated areas. About 31% of the vegetation on the QTP exhibited significant “greening”, primarily in the northern plateau. This greening was attributed not only to improvements in climate-induced hydrothermal conditions but also to the effective implementation of ecological projects, which account for roughly half of the significant greening. Only 2% of the vegetation on the QTP showed significant “browning”, sporadically distributed in the southern plateau and the Sanjiangyuan region. In these areas, besides climate-induced drought intensification, approximately 78% of the significant browning was due to unreasonable grassland utilization and intense human activities. The area where precipitation dominates vegetation improvement was larger than the area dominated by temperature, whereas the area where precipitation dominates vegetation degradation is smaller than that where temperature dominates degradation. The implementation of a series of ecological projects has resulted in a much larger area where human activities positively promoted vegetation compared to the area where they negatively inhibited it.

Reducing nitrogen fertilizer application highlights its role in optimizing soil bacterial communities to achieve sustainable agriculture. However, the specific mechanisms of bacterial community change under these conditions are not yet clear. In this study, we employed long-term field experiments and high-throughput sequencing to analyze how varying levels of nitrogen application influence the soil bacterial community structure and co-occurrence networks. The results show that reducing the nitrogen inputs significantly enhances the diversity and evenness of the soil bacterial communities, possibly due to the diminished dominance of nitrogen-sensitive taxa, which in turn liberates the ecological niches for less competitive species. Furthermore, changes in the complexity and stability of the bacterial co-occurrence networks suggest increased community resilience and a shift toward more mutualistic interactions. These findings underline the potential of reduced nitrogen application to alleviate competitive pressures among bacterial species, thereby promoting a more diverse and stable microbial ecosystem, highlighting the role of competitive release in fostering microbial diversity. This research contributes to our understanding of how nitrogen management can influence soil health and offers insights into sustainable agricultural practices.

This study analyzed the applicability of five long-term precipitation datasets in the Hang-Jia-Hu Plain of eastern China based on meteorological observation data. The accuracy of each dataset at different time scales (yearly, monthly) was analyzed. Besides, their spatiotemporal distributions and differences in precipitation event frequency were also compared. The results indicate that the high-resolution (1 day, 1 km) and long-term (1961–2019) gridded dataset for temperature and precipitation across China (HRLT) exhibited the best overall performance at the annual scale, while the long-term, gauge-based gridded precipitation dataset for the Chinese mainland (CHM_PRE) performed the best at the monthly scale. The dataset of monthly precipitation with a resolution of 1 km in China from 1960 to 2020 (HHU) and the China Meteorological Forcing Dataset (CMFD) tend to overestimate the local precipitation amounts, while the other three products are characterized by an underestimation. The mean result of the five datasets indicates a slight, statistically insignificant rise in precipitation, by 4.19 mm annually, with an overall multi-year average of 1303.28 mm. The analysis of the five datasets successfully captures the spatial precipitation patterns across the Hang-Jia-Hu Plain, characterized by higher precipitation levels in the southwest and lower in the northeast. Although the interannual variability displays general consistency, there are significant discrepancies in the interannual growth rates and the spatial distribution of significance across different regions. This study can provide a reference for the accuracy of precipitation data in the fields of hydrology, meteorology, agriculture, and ecology, facilitating the analysis of uncertainties in related research.

With an advancement in global climate change, the frequency of extreme climatic events, such as high temperature, drought, and flooding, has increased. Meanwhile, outbreaks of crop fungal diseases are becoming more frequent and serious, and crop growth and food production are seriously threatened. This article focuses on the climate change-related aggravation of crop fungal diseases; summarizes the progress in research on the impact of climate change on soil-borne fungal diseases, air-borne fungal diseases, and seed-borne fungal diseases; and discusses the conventional methods for diagnosing crop fungal diseases. On the basis of comparative analysis, the concept of ecological control is proposed; ecological control can maintain the dynamic balance of crop–biology–soil, provide a good environment for the healthy growth of crops, and provide a new fungal disease control method in the context of climate change.

Straw returning may stimulate soil microbial activity, thereby influencing microbial-mediated soil nitrification, which can lead to nitrate leaching and nitrogen (N) loss. However, its effects under long-term nitrogen fertilization remain unclear. At an experimental station with 34 years of fertilizer application (0, 135, and 270 kg ha−1 N), we investigated how nitrogen fertilization and straw returning affected the soil potential nitrification rate (PNR) and ammonia-oxidizing microorganisms (AOM). Our results suggest that N fertilization concurrently inhibits soil PNR, but this inhibition can be alleviated by straw returning, particularly with low nitrogen fertilization (p < 0.05). Long-term N fertilization significantly decreased the abundance of ammonia-oxidizing archaea (AOA), ammonia-oxidizing bacteria (AOB), and complete ammonia-oxidizing bacteria cladeB (CAOB-cladeB). Straw returning increased AOA abundance and diversity, especially with low or no fertilization (p < 0.05). Furthermore, the partial least squares path model demonstrated that AOA abundance affected soil PNR by altering the AOA community. According to random forest analysis, soil pH and AOA beta diversity were the primary factors affecting soil PNR (explaining 10.76% and 10.03% of the variation, respectively). Overall, our findings highlight the importance of straw returning and AOA in soil nitrification under long-term nitrogen fertilization, emphasizing the need to consider these interactions for sustainable agriculture.

To study the polarization reflection characteristics of metal surfaces, a parameter optimization method for the polarization bidirectional reflection distribution function (pBRDF) model of metal surfaces based on the improved strawberry algorithm has been proposed. Firstly, the light scattering characteristics of metal surfaces were analyzed and a multi-parameter pBRDF model was constructed. Then, the working mechanism of the strawberry optimization algorithm was investigated and improved by introducing the chaotic mapping and Levy flight strategy to overcome the shortcomings, such as low convergence rate and easily falling into local optimum. Finally, the method proposed in this paper was validated by simulating open-source data from references and the obtained ones with a self-built experimental platform. The results show that the proposed method outperforms those by nonlinear least squares, particle swarm optimization and the original strawberry algorithm in fitting the detected degree of polarization (DOP) data, indicating the modeling accuracy was significantly improved and better suited to characterize the polarized reflection properties of metal surfaces.

In this paper, by replacing the exponential memory kernel function of a tabu learning single-neuron model with the power-law memory kernel function, a novel Caputo’s fractional-order tabu learning single-neuron model and a network of two interacting fractional-order tabu learning neurons are constructed firstly. Different from the integer-order tabu learning model, the order of the fractional-order derivative is used to measure the neuron’s memory decay rate and then the stabilities of the models are evaluated by the eigenvalues of the Jacobian matrix at the equilibrium point of the fractional-order models. By choosing the memory decay rate (or the order of the fractional-order derivative) as the bifurcation parameter, it is proved that Hopf bifurcation occurs in the fractional-order tabu learning single-neuron model where the value of bifurcation point in the fractional-order model is smaller than the integer-order model’s. By numerical simulations, it is shown that the fractional-order network with a lower memory decay rate is capable of producing tangent bifurcation as the learning rate increases from 0 to 0.4. When the learning rate is fixed and the memory decay increases, the fractional-order network enters into frequency synchronization firstly and then enters into amplitude synchronization. During the synchronization process, the oscillation frequency of the fractional-order tabu learning two-neuron network increases with an increase in the memory decay rate. This implies that the higher the memory decay rate of neurons, the higher the learning frequency will be.

Based on five periods of Landsat remote sensing data from 1980 to 2020, this study constructs a landscape ecological risk-ecosystem service value evaluation model and integrates it with a geodetector model to analyse the environmental benefits of the development of the “production–living–ecological space” pattern and its driving factors in the Xuzhou planning area. The results of the study are as follows: (1) Over the past 40 years, the expansion of living spaces has significantly encroached upon adjacent agricultural production areas and ecological spaces, such as forests and grasslands. Specifically, the areas of agricultural land, forests, and grassland have been diminished by 277.39 km2, 23.8 km2 and 12.93 km2, respectively; in contrast, urban and rural living spaces have increased by 238.62 km2 and 58.92 km2, alongside a rise in industrial production areas, water bodies, and other ecological spaces. (2) Throughout the 40-year period, both the landscape ecological risk (ERI) and ecosystem service value (ESV) in the study area have shown a decreasing trend. The proportion of high- and medium-high-risk areas of the ERI have decreased by 5.19% and 7.50%, respectively, while low, lower, and medium ecological risk areas have increased by 6.40%, 3.22% and 3.07%, respectively. In addition, low-ESV areas have increased by 14.22%, while the proportion of high- and medium-high-ESV areas have decreased by 1.16%. (3) There is a significant positive spatial correlation between the ERI and ESV. Regions with dense ecological spaces comprising forests, water bodies, and grasslands, particularly in the northeastern part of the Jiawang District and the southeastern part of the Tongshan District, demonstrate superior regional ecosystem service quality. The ERI and ESV are dominated by “high–high” and “low–high” aggregation. Conversely, in the southwestern part of the study area, the expansion of living space has led to the transformation of some agricultural land, forest land, and grassland into less risky construction land, resulting in a decline in the quality of regional ecosystem services. The local spatial correlation between the ERI and ESV changed from “high–high”, “low–low”, “low–high” agglomeration to “low–low” agglomeration. (4) Key factors influencing the spatial differentiation of the “production–living–ecological space” include the GDP, population density, soil type, and the distance to towns and roads. Among these, the interaction between population density and soil type has the most significant effect on the changes in the pattern of the “production–living–ecological space”.

This study explored the potential of saponins from Korean Red Ginseng to target the PINK1/Parkin mitophagy pathway, aiming to enhance insulin sensitivity in hepatocytes—a key factor in metabolic disorders like metabolic dysfunction-associated steatotic liver disease (MASLD) and type 2 diabetes. Results from both in vitro and in vivo experiments showed increased expression of PINK1 and Parkin, activating mitophagy and reducing oxidative stress through reduction in mitochondrial and total reactive oxygen species. Additionally, improvements in insulin signaling were observed, including the upregulation of phosphorylated IRS and AKT, and downregulation of gluconeogenic enzymes, underscoring the saponins’ efficacy in boosting insulin sensitivity. The findings highlighted Korean Red Ginseng-derived saponins as potential treatments for insulin resistance and related metabolic conditions.

To investigate the shear performance of assembled bamboo scrimber (BS)-lightweight concrete (LC) connection systems, three groups of nine BS-LC shear connections were fabricated in this work using BS, LC, dowels, and grout. The experimental parameters included the dowel diameter and fabrication process (cast-in-place vs. assembly). Push-out tests were conducted on the specimens, and traditional linear variable displacement transducer (LVDT) measurements and the advanced digital image correlation (DIC) technique were employed to determine performance indicators such as the cross-section slip of composite members. Subsequently, the method for calculating the shear capacity of assembled BS-LC connection systems was theoretically analyzed. The research results showed that the load-slip curves measured by DIC were highly correlated with those measured by LVDT, thus, validating the reliability of the DIC data. According to the DIC data, the variations in slip of the shear connection over the interface height were further analyzed. An equation for calculating the shear capacity of dowel shear connectors was proposed based on theoretical analysis with comprehensive consideration of the experimental indicators such as the failure mode, load-slip curve, shear stiffness, and shear capacity of the specimens. The theoretical calculation values were in good agreement with the experimental results.

Instance segmentation of remote sensing images can not only provide object-level positioning information but also provide pixel-level positioning information. This pixel-level information annotation has a wide range of uses in the field of remote sensing, and it is of great value for environmental detection and resource management. Because optical images generally have complex terrain environments and changeable object shapes, SAR images are affected by complex scattering phenomena, and the mask quality obtained by the traditional instance segmentation method used in remote sensing images is not high. Therefore, it is a challenging task to improve the mask quality of instance segmentation in remote sensing images. Since the traditional two-stage instance segmentation method consists of backbone, neck, bbox head, and mask head, the final mask quality depends on the product of all front-end work quality. Therefore, we consider the difficulty of optical and SAR images to bring instance segmentation to the targeted improvement of the neck, bbox head, and mask head, and we propose the Context-Aggregated and SAM-Guided Network (CSNet). In this network, the plain feature fusion pyramid network (PFFPN) can generate a pyramid for the plain feature and provide a feature map of the appropriate instance scale for detection and segmentation. The network also includes a context aggregation bbox head (CABH), which uses the context information and instance information around the instance to solve the problem of missed detection and false detection in detection. The network also has a SAM-Guided mask head (SGMH), which learns by using SAM as a teacher, and uses the knowledge learned to improve the edge of the mask. Experimental results show that CSNet significantly improves the quality of masks generated under optical and SAR images, and CSNet achieves 5.1% and 3.2% AP increments compared with other SOTA models.

Patulin, an emerging mycotoxin with high toxicity, poses great risks to public health. Considering the poor antibody production in patulin immunization, this study focuses on the four-dimensional data-independent acquisition (4D-DIA) quantitative proteomics to reveal the immune response of patulin in rabbits. The rabbit immunization was performed with the complete developed antigens of patulin, followed by the identification of the immune serum. A total of 554 differential proteins, including 292 up-regulated proteins and 262 down-regulated proteins, were screened; the differential proteins were annotated; and functional enrichment analysis was performed. The differential proteins were associated with the pathways of metabolism, gene information processing, environmental information processing, cellular processes, and organismal systems. The functional enrichment analysis indicated that the immunization procedures mostly resulted in the regulation of biochemical metabolic and signal transduction pathways, including the biosynthesis of amino acid (glycine, serine, and threonine), ascorbate, and aldarate metabolism; fatty acid degradation; and antigen processing and presentation. The 14 key proteins with high connectivity included G1U9T1, B6V9S9, G1SCN8, G1TMS5, G1U9U0, A0A0G2JH20, G1SR03, A0A5F9DAT4, G1SSA2, G1SZ14, G1T670, P30947, P29694, and A0A5F9C804, which were obtained by the analysis of protein–protein interaction networks. This study could provide potential directions for protein interaction and antibody production for food hazards in animal immunization.

The epidermal growth factor receptor (EGFR) is a pivotal target in cancer therapy due to its significance within the tyrosine kinase family. EGFR inhibitors like AG-1478 and PD153035, featuring a 4-anilinoquinazoline moiety, have garnered global attention for their potent therapeutic activities. While pre-clinical studies have highlighted the significant impact of halogen substitution at the C3’-anilino position on drug potency, the underlying mechanism remains unclear. This study investigates the influence of halogen substitution (X = H, F, Cl, Br, I) on the structure, properties, and spectroscopy of halogen-substituted 4-anilinoquinazoline tyrosine kinase inhibitors (TKIs) using time-dependent density functional methods (TD-DFT) with the B3LYP functional. Our calculations revealed that halogen substitution did not induce significant changes in the three-dimensional conformation of the TKIs but led to noticeable alterations in electronic properties, such as dipole moment and spatial extent, impacting interactions at the EGFR binding site. The UV–visible spectra show that more potent TKI-X compounds typically have shorter wavelengths, with bromine’s peak wavelength at 326.71 nm and hydrogen, with the lowest IC50 nM, shifting its lambda max to 333.17 nm, indicating a correlation between potency and spectral characteristics. Further analysis of the four lowest-lying conformers of each TKI-X, along with their crystal structures from the EGFR database, confirms that the most potent conformer is often not the global minimum structure but one of the low-lying conformers. The more potent TKI-Cl and TKI-Br exhibit larger deviations (RMSD > 0.65 Å) from their global minimum structures compared to other TKI-X (RMSD < 0.15 Å), indicating that potency is associated with greater flexibility. Dipole moments of TKI-X correlate with drug potency (ln(IC50 nM)), with TKI-Cl and TKI-Br showing significantly higher dipole moments (>8.0 Debye) in both their global minimum and crystal structures. Additionally, optical spectral shifts correlate with potency, as TKI-Cl and TKI-Br exhibit blue shifts from their global minimum structures, in contrast to other TKI-X. This suggests that optical reporting can effectively probe drug potency and conformation changes.

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease which seriously affects public health. Gut microbiota remains a dynamic balance state in healthy individuals, and its disorder may affect health status and even results in metabolic diseases. Quercetin, a natural flavonoid, has been shown to have biological activities that can be used in the prevention and treatment of metabolic diseases. This study aimed to explore the mechanism of quercetin in alleviating T2DM based on gut microbiota. db/db mice were adopted as the model for T2DM in this study. After 10 weeks of administration, quercetin could significantly decrease the levels of body weight, fasting blood glucose (FBG), serum insulin (INS), the homeostasis model assessment of insulin resistance (HOMA-IR), monocyte chemoattractant protein-1 (MCP-1), D-lactic acid (D-LA), and lipopolysaccharide (LPS) in db/db mice. 16S rRNA gene sequencing and untargeted metabolomics analysis were performed to compare the differences of gut microbiota and metabolites among the groups. The results demonstrated that quercetin decreased the abundance of Proteobacteria, Bacteroides, Escherichia-Shigella and Escherichia_coli. Moreover, metabolomics analysis showed that the levels of L-Dopa and S-Adenosyl-L-methionine (SAM) were significantly increased, but 3-Methoxytyramine (3-MET), L-Aspartic acid, L-Glutamic acid, and Androstenedione were significantly decreased under quercetin intervention. Taken together, quercetin could exert its hypoglycemic effect, alleviate insulin resistance, repair the intestinal barrier, remodel the intestinal microbiota, and alter the metabolites of db/db mice.

As the Internet of Things (IoT) continues to expand, billions of IoT devices are now connected to the internet, producing vast quantities of data. Collecting and sharing this data has become crucial to improving IoT technologies and developing new applications. However, the publication of privacy-preserving IoT traffic data is exceedingly challenging due to the various privacy concerns surrounding users, IoT networks, and devices. In this paper, we propose a data transformation method aimed at safeguarding the privacy of IoT devices by transforming time series datasets. Based on our measurements, we have found that the transformed datasets retain the intrinsic value of the original IoT data and maintains data utility. This approach will enable non-expert data owners to better understand and evaluate the potential device-level privacy risks associated with their IoT data while simultaneously offering a reliable solution to mitigate their concerns about privacy violations.