Impact of sensory afferences in postural control quantified by force platform in healthy older adults: Systematic review and meta-analysis.

Frequency domain analysis for identifying dominant segregation units in a chain of material handling processes: A cellular automaton framework

Streptococcus iniae is a gram-positive, spherical- or ovoid-shaped, facultative anaerobic bacterium and is one of the major species causing streptococcosis, resulting in economic losses in aquaculture. Endolysins, peptidoglycan hydrolases produced by bacteriophages, are emerging as replacements for antibiotics due to their specific lytic activity against pathogens. This study aimed to develop a novel endolysin, SinLys1930, that specifically targets and kills S. iniae. The molecular and structural characteristics of SinLys1930 were predicted based on bioinformatic approaches. The lytic activity of SinLys1930 was evaluated against S. iniae KCTC 3657 under various conditions, including different dosages, pH levels, temperatures, NaCl concentrations, and metal ions, to identify the optimal conditions, and its effectiveness was further tested in sterilized seawater. The conserved domain analysis revealed that SinLys1930 possesses two enzymatically active domains (NlpC/P60 and glucosaminidase superfamilies) with two cell wall-binding domains (CW-7 superfamily) positioned between them. The lytic activity of SinLys1930 was highest at pH 6.0 to 6.5 and temperatures between 16 and 37°C, and it was maintained even under high NaCl concentration. SinLys1930 reduced the optical density of S. iniae in sterilized seawater by approximately 60% after incubation for 1 h. Therefore, SinLys1930 could potentially serve as an alternative to antibiotics for preventing streptococcosis caused by S. iniae in the aquaculture industry.

Serine proteases represent a significant family of proteolytic enzymes, characterized by their serine-dependent catalytic mechanism. These enzymes are integral to various biological processes, including fungal growth, development, and pathogenicity. Despite their importance, the sequence characterization and expression patterns of this protein family in Setosphaeria turcica are not yet fully characterized and remain underexplored. A total of 74 putative serine protease family proteins (StSPs) were identified in S. turcica and classified into 12 subfamilies based on phylogenetic analysis. Structural domain analysis revealed that 24 StSPs contain signal peptides, of which five were experimentally validated as secretory proteins through yeast secretion assays. Expression profiling using RNA-seq data demonstrated that StSPs exhibit distinct expression patterns across different developmental and infection stages, with 61 genes showing high expression during critical infection phases. The expression levels of nine genes were validated via qRT-PCR, and the results were consistent with the RNA-seq data. Among these proteins, StSP8-4 demonstrated elevated expression during the course of fungal infection. Functional characterization of StSP8-4 OE and RNAi strains revealed that this gene plays a crucial role in maintaining fungal pathogenicity, although silencing did not impair conidium or hyphal development. These findings provide valuable insights for further research on serine protease genes in S. turcica.

The subject matter of the article is the problem of detecting fabricated information in socially oriented systems characterized by significant user load. The goal of the work is to develop of a two-layer fake information classification model based on a combination of a naive Bayesian classifier and a hybrid recurrent-convolutional neural network. The following tasks were solved in the article: conducting expert evaluation and domain analysis to determine basic classes of fake information; analyzing linguistic markers of disinformation and developing feature vectors for classification; developing models for data segregation using a naive Bayesian classifier; conducting experimental verification of the proposed two-layer model in comparison with the RCNN approach. The following methods used are – analytical method for forming a set of disinformation markers; inductive method for determining the target set of indicators for implementing the second layer of the model; expert evaluation for determining the most influential efficiency factors and feature weight coefficients; experimental and multi-criteria evaluation methods for determining the most effective model. The following results were obtained – a classification structure for types of fake information was formed, including five categories from jokes to globally harmful news. A set of discriminative features characteristic of fabricated information was developed, including primary linguistic markers and secondary stylometric indicators. It was determined that the approach using a two-layer model demonstrated, on average, a 15% improvement in efficiency compared to direct application of a hybrid recurrent-convolutional neural network. Conclusions: the application of a two-layer data classification model successfully expands the capabilities of basic detection of data falsification, including scale assessment and analysis of fabrication intentionality. Empirical analysis shows that implementation of a two-layer model with a naive Bayesian classifier achieves an average 15% performance improvement compared to simple neural network application. This performance difference becomes particularly significant in high-throughput systems where rapid identification and response to fabricated information are critical operational parameters. The obtained result allows us to assert the feasibility of implementing the proposed approach, and accordingly, provides the opportunity to reduce the impact of such information in socially oriented systems, especially during crisis situations.

Cystic fibrosis (CF) is a life-shortening genetic disorder caused by mutations in the CFTR gene, leading to dysfunctional chloride ion transport and associated complications. This study employs computational chemistry to explore the molecular mechanisms underlying CF and to aid the development of targeted therapeutics. Twenty-seven ligand derivatives from 3-(2-benzyloxyphenyl) isoxazoles and isoxazolines previously reported as CFTR activators were analyzed and compared to genistein, a known CF therapeutic. Ligand structures were optimized using Density Functional Theory (DFT), and molecular descriptors were computed with PaDEL software. Using Genetic Function Approximation (GFA) via Material Studio, Quantitative Structure-Activity Relationship (QSAR) models were developed to correlate molecular features with biological activity (pIC₅₀). The top model (R² = 0.974) demonstrated high predictive power and reliability, validated through cross-validation and applicability domain analysis. Key descriptors such as SpDiam_Dt, GATS4v, and MATS7i significantly influenced model performance, offering insights into molecular traits critical for CF treatment efficacy. These findings highlight the potential of computational approaches in accelerating drug discovery for cystic fibrosis by identifying and optimizing promising lead compounds.

This study investigates the relationship between geopolitical risks and inflation in Turkey over the period 1985-2023, employing a time-frequency analysis approach. The study utilizes the Global Geopolitical Risk Index (GPR) as the indicator of geopolitical risk and the Consumer Price Index (CPI) as the indicator of inflation. Employing continuous (CWT), cross (XWT), and coherence (WTC) wavelet analyses, the study examines the intrinsic fluctuations of the variables, their co-movements, and correlations within the time-frequency domain. The findings from the wavelet analysis indicate a generally positive relationship between geopolitical risks and inflation in Turkey, which fluctuates across time and different frequencies. Notably, periods such as the Gulf War in the early 1990s, the 2001 economic crisis, and the post-2018 period exhibited a strong association between increased geopolitical risks and inflation. During these periods, significant co-movements (XWT) and high coherence (WTC) were identified between the variables, alongside short-to-medium-term cycles. In contrast, during the period of relative stability in the mid-2000s, the influence of geopolitical risks on inflation diminished. However, following 2018, the relationship has strengthened again, concurrent with an escalation in geopolitical risks faced by Turkey. The study's conclusions underscore the significance of considering geopolitical risks, exchange rate stability, energy and food security, the management of inflation expectations, and structural reforms in addressing inflation. The research contributes uniquely to the literature on the Turkish economy, by presenting a time-frequency domain analysis.

Making advancements in Quantitative Structure-Activity Relationship (QSAR) modeling is crucial for predicting biological activities in new compounds. Traditional 2D-QSAR and 3D-QSAR methods often face challenges in terms of computational efficiency and predictive accuracy. This study introduces a machine learning approach using 3D Discrete Tchebichef Moments (3D-DTM) to address these issues. The 3D-DTM method offers efficient computation, robust descriptor generation, and improved interpretability, making it a promising alternative to conventional QSAR techniques. By capturing global 3D shape information, this method provides better representation of molecular interactions essential for biological activities. We applied the 3D-DTM model to a dataset of 46 molecules derived from the Dihydrophenanthrene scaffold, screened against the enzymatic activity of 3-chymotrypsin-like protease, a key antiviral target. Principal Component Analysis and k-means clustering refined descriptors, followed by stepwise Multiple Linear Regression (step-MLR), Partial Least Squares Regression (PLS-R), and Feed-Forward Neural Network (FFNN) techniques for 3DTMs-QSAR model development. The results showed high correlation and predictive accuracy, with significant validation from internal and external tests. The step-MLR model emerged as the optimal method due to its balance of predictive power and simplicity. Validation through y-Randomization and applicability domain analysis confirmed the model’s robustness. Virtual screening of 100 novel compounds identified 32 with improved pIC50 values. This study highlights the potential of 3D-DTMs in QSAR modeling, providing a scalable and reliable tool for computational chemistry and drug discovery. A user-friendly software tool was also developed to facilitate 3D-DTM extraction from input 3D molecular images.

This paper examines how to optimize the human resource management structure of the Kazakh Research Institute of Oncology and Radiology as workload grows and compliance and digitalization demands increase. The aim is to propose a target HR operating model that improves the speed and quality of people decisions while staying compliant. The methodology combines process analysis of key HR domains (workforce planning, recruitment, onboarding, learning, performance, rewards, and HR administration), a RACI responsibility matrix, identification of duplication and cycle time losses, and a KPI framework aligned with ISO 30414. The results include a target organizational design integrating HR shared services, an HR business partner role for managerial support, and centers of expertise for talent acquisition, development, motivation, and analytics. A KPI set and an implementation roadmap with change and risk management are provided. Expected outcomes include shorter time to hire, lower turnover, and higher staff satisfaction across key institute teams.

With the increasing prevalence of antibiotic-resistant bacteria, phage therapy has garnered significant attention. Holin and lysin play essential roles in the phage-induced lysis of bacteria. This study investigated the functions of the holin protein Hol 46 and the lysozyme protein Lys 17 from the phage PZL-Ah152 and the mechanisms underlying the action of the fusion protein Hol 46_Lys 17. Assays, including membrane protein extraction tests and fluorescence microscopy, verified that the Hol 46 protein localized to the cell membrane and significantly inhibited the growth of Aeromonas hydrophila Ah152. We determined that the Hol 46 C-terminal domain and glutamic acid residue at position 66, along with the lysine residues at positions 63 and 64, were critical for its cell-penetrating activity. Furthermore, the Hol 46_Lys 17 fusion protein was developed to combine the membrane-disrupting capacity of Hol 46 with the lytic action of Lys 17. Hol 46_Lys 17 exhibited not only broader antibacterial effects against Aeromonas (24/38) but also against Escherichia coli (3/12) and Salmonella (5/29). Transcriptomic studies revealed that treatment with Hol 46_Lys 17 led to significant changes in the expression of genes related to flagellar synthesis, bacterial chemotaxis, and TCSs. Finally, animal studies were performed to confirm the safety and effectiveness of Hol 46_Lys 17 in treating intestinal infections caused by A. hydrophila in crucian carp. Our data showed that phage lytic system-related proteins hold great potential for the treatment of infections caused by antibiotic-resistant bacteria.IMPORTANCEAs a zoonotic and fish-pathogenic bacterium, Aeromonas hydrophila causes significant harm worldwide. Owing to the emergence of A. hydrophila strains with multidrug resistance, phage therapy has garnered extensive attention. Holin and lysin, phage-derived antibacterial proteins, play crucial roles in antimicrobial activity. The glutamic acid at position 66 and lysine residues at positions 63 and 64 in the C-terminal domain of the Hol 46 protein from phage PZL-Ah152 were essential for its A. hydrophila cell-penetrating activity. The Hol 46_Lys 17 fusion protein exhibited broad-spectrum antibacterial activity, including effects against Salmonella and Escherichia coli. Transcriptomic assays further revealed the effects of Hol 46_Lys 17 on A. hydrophila Ah152 at the molecular level. In vivo studies confirmed its efficacy and safety for the treatment of intestinal infections in crucian carp.

Background: The interaction between epidermal growth factor (EGF) and its receptor (EGFR) is a critical process in wound healing, owing to their role in initiating epidermal and dermal regeneration. EGF-based therapies enhance wound healing, and engineered EGF mutants with increased EGFR binding are designed to further improve this effect. Objectives: This study aimed to utilize molecular dynamics simulations (MDS) to comprehensively analyze the interactions between EGF mutants and EGFR, providing mechanistic insights that could inform the design of novel therapeutics for wound healing and regenerative medicine. Methods: Molecular dynamics simulations were performed to investigate the key molecular interactions and binding dynamics between wild-type EGF (WT-EGF) and two engineered EGF mutants (m28-EGF and m123-EGF) in complex with EGFR. Comparative analyses of structural stability, binding affinity, and interaction energy were conducted to elucidate the molecular basis of enhanced receptor activation by these mutants. Results: Molecular dynamics simulation results indicated that the introduced mutations rendered the EGF mutants less flexible, allowing them to adopt more compact and stable structures. Accordingly, the mutations in m28-EGF and m123-EGF significantly affected their binding affinity to EGFR, with m28-EGF exhibiting a preference for domain I and m123-EGF showing a preference for domain III of EGFR. Additionally, analysis of the EGFR dimerization domain revealed that the EGFR chains in the mutant complexes demonstrated an increased capacity to form hydrogen bonds and hydrophobic interactions compared to the wild type. Notably, the calculated interaction energies in the m123-EGF/EGFR complex were higher than those in other complexes, indicating a stronger binding affinity. These findings underscore the pivotal contribution of domain II to EGFR dimer stability and its essential role in maintaining the EGF–EGFR interaction. Conclusions: These results suggest that EGF super-agonists hold significant promise in regenerative medicine and that targeting ligand–receptor interactions can facilitate therapeutic development to modulate EGFR signaling. Understanding how ligands induce conformational changes in EGFR could lead to new treatments and personalized medicine for EGFR-related diseases, although the diverse effects of different ligands warrant further investigation.

In modern industry, thin-walled components have become critical elements in high-end manufacturing due to their unique structure and lightweight properties. However, their production process is prone to chatter, which severely impacts machining quality and efficiency. Although ultrasonic vibration grinding technology can partially suppress chatter, the issue remains unresolved. To address this, this paper proposes an online chatter monitoring method based on an improved convolutional neural network (ICS-CNN) and the Sparrow Optimization Algorithm (SSA). This approach enhances key information capture through multi-scale feature extraction and attention mechanisms, while incorporating residual connections and a feature pyramid structure to strengthen the model's ability to identify subtle chatter characteristics. Input signals undergo frequency domain analysis and filtering to improve data quality. The SSA algorithm is further employed to optimise network parameters, constructing the SSA-ICS-CNN intelligent monitoring model. Experimental results demonstrate an identification accuracy of 98.37 % with a decision time of merely 147 milliseconds, while visualisation techniques validate its discrimination precision. Compared to conventional convolutional neural networks, this approach achieves significant improvements in both recognition accuracy and response speed, effectively overcoming limitations inherent in traditional methods reliant on manual feature extraction and dynamic response delays.

Cottonseed oil is rich in unsaturated fatty acids (UFAs), making it suitable for use as edible oil. Fatty acid desaturases (FADs) play a major role in the conversion of monounsaturated fatty acids (MUFAs) to polyunsaturated fatty acids (PUFAs). In total, 39 GhFAD genes were detected in upland cotton and divided into five groups in the present study. Gene structure and domain analysis showed that GhFAD members within each group were highly conserved. Cis-elements associated with environmental stress and hormone responses were identified in GhFAD promoters. The predicted transcription factors and miRNAs targeting these genes suggest extensive roles for GhFADs in diverse stress conditions. Analysis of expression profiles indicated that GhFAD genes participate extensively in developmental processes and stress adaptation in cotton. Among these, the concurrent high expression of GhFAD2-1 and low expression of GhFAD3 are consistent with the typical fatty acid profile of cottonseed oil. GhFAD3-2 and GhFAD3-1 exhibit a complementary expression profiles, suggesting they may operate in a relay manner during fiber development. Additionally, experimental evidence established that GhFAD2-3 is involved in the cold stress response. This research delivers a thorough characterization of the GhFAD genes in upland cotton, thereby establishing a solid groundwork for future functional genomics studies.

This paper investigates Fourier series and Fourier transforms as fundamental tools for electrical circuit analysis with an emphasis on bridging mathematical theory and practical applications. An analytical applied approach is adopted in which periodic and non-periodic signals are represented in the frequency domain to facilitate the analysis of circuit responses to various voltage and current excitations. The study presents the mathematical foundations of Fourier series and Fourier transforms, followed by the frequency domain analysis of RLC circuits and an examination of key phenomena associated with spectral analysis. To enhance practical insight, Mathcad was employed to generate graphical representations of the signals corresponding to each series and to demonstrate the effect of the number of harmonics on waveform reconstruction accuracy. The results demonstrate that Fourier based analysis, when supported by graphical visualization offers a clearer and more efficient interpretation of circuit behavior compared with conventional time-domain methods. Moreover, this approach provides a comprehensive framework for the design and analysis of electronic circuits, electrical filters, communication systems, and signal processing applications.

Central nervous system (CNS) metastasis is a major driver of morbidity in metastatic breast cancer, yet the molecular determinants of CNS tropism remain incompletely defined. LYN, a Src-family kinase integrating receptor tyrosine kinase and integrin signaling, is a biologically plausible mediator of metastatic traits. We performed a retrospective, multi-study analysis of publicly available breast cancer cohorts aggregated in cBioPortal. After harmonization and de-duplication, LYN status was determinable in 5,947 invasive carcinoma of no special type (NST) tumors across 29 studies. The primary endpoint was CNS metastasis at any time (Yes/No), harmonized via a prespecified controlled vocabulary (case-insensitive substring mapping). Somatic LYN variants (coding SNVs/indels) were collapsed to patient-level classes (missense-only; truncating if any nonsense/frameshift/splice). Variants with resolvable positions were mapped to Src-family modules (SH4/Unique, SH3, SH2, SH2-kinase linker, kinase). Two-group comparisons used two-sided Fisher's exact tests with exact 95% CIs; domain screens used omnibus χ² and Benjamini-Hochberg FDR control. A prespecified Firth logistic model evaluated truncating vs. missense within LYN-mutant tumors. SETTING: Public cancer genomics repositories (cBioPortal); multi-institutional cohorts. 5,947 tumors across multi-study cohorts with LYN status available. None. Primary: ever-CNS metastasis (yes/no). Secondary: distribution of LYN variant classes and domains (SH4/Unique, SH3, SH2, linker, kinase). RESULTS: CNS metastasis occurred in 5/46 (10.9%) LYN-mutated tumors vs. 110/5,901 (1.9%) LYN wild-type tumors (odds ratio (OR) = 6.42; 95% confidence interval (CI), 2.49-16.56; p = 0.0018). The endpoint was captured as ever vs. never CNS involvement (event dates unavailable), precluding time-to-event inference. Within LYN-mutant cases, an exploratory domain analysis indicated that distributions differed by CNS status (omnibus χ² p ≈ 0.014); a one-versus-rest signal at the SH4/Unique N-terminus was nominally significant and borderline after false discovery rate (FDR) (unadjusted p ≈ 0.010; q ≈ 0.052; small in-domain n = 3). By mutation class, truncating vs. missense showed a higher CNS-positive proportion (28.6% vs. 7.9%) but did not reach significance (Fisher p = 0.166; alternatively framed OR = 4.22; exact 95% CI, 0.58-30.75; p = 0.182). Firth estimates were directionally consistent with wide profile CIs under sparse counts. Across pooled cohorts, LYN mutation is associated with increased odds of CNS metastasis, and domain context appears informative, with a small-sample, FDR-borderline enrichment at the SH4/Unique N-terminus. The truncating-class signal is exploratory given limited power. Signals by domain (notably SH4/Unique) are exploratory and require independent validation in larger, uniformly annotated datasets. Given small mutant denominators and ever-CNS endpoint capture, findings are hypothesis-generating and not actionable for risk stratification or treatment selection. Results motivate domain-aware annotation in future validation studies and mechanistic work.

Protein fragmentation offers an effective approach for resolving localized conformational information and mapping discrete structural domains, yet current techniques may miss their dynamic behavior under changing external environments. Here, we harness nanopore sensing to interrogate 50-residue fragments from N-terminal (NTD) and C-terminal domains (CTD) of the SARS-CoV-2 nucleocapsid (N) protein under physiological conditions. We propose utilizing fractional current blockade (ΔI/I0) as a metric for the conformational spectrum, enabling us to probe the dynamics and aggregation behavior of protein fragments across various conditions. By systematically varying the voltage (70-300 mV) and employing complementary molecular dynamics simulations, we observe significant alterations in ΔI/I0 and translocation duration (τ), which indicate distinct domain-specific behaviors. Notably, the CTD fragment exhibits dimerization at lower voltages, followed by dissociation at elevated voltages, thus highlighting the capability of nanopore assays to resolve dynamic single-molecule transitions in real time. Additionally, we elucidate the electric field-dependent dimer dissociation behavior using size-variant nanopores and artificially modulate the environment of the CTD fragments to explore the factors affecting fragment dimerization. The full width at half maximum (FWHM) of the fitted conformational spectrum is employed to assess protein conformational flexibility and stability, influenced by voltage and ionic strength. Our findings not only reveal electric field-driven conformational plasticity of N-protein but also advance nanopore-based strategies for real-time protein domain analysis, informing antiviral therapeutic and diagnostic development.

Frequency domain analysis reveals fundamental image patterns difficult to observe in raw pixel values, while avoiding redundant information in original image processing. Although recent remote sensing foundation models (FMs) have made progress in leveraging spatial and spectral information, they have limitations in fully utilizing frequency characteristics that capture hidden features. Existing FMs that incorporate frequency properties often struggle to maintain connections with the original image content, creating a semantic gap that affects downstream performance. To address these challenges, we propose the All-in-One Spectral-Spatial-Frequency Awareness Foundation Model (Alliance), a framework that effectively integrates information across all three domains. Alliance introduces several key innovations: (1) a progressive frequency decoding mechanism inspired by human visual cognition that minimizes multi-domain information gaps while preserving connections between general image information and frequency characteristics, progressively reconstructing from low to mid to high frequencies to extract patterns difficult to observe in raw pixel values; (2) a triple-domain fusion attention module that separately processes amplitude, phase, and spectral-spatial relationships for comprehensive feature integration; and (3) frequency embedding with frequency-aware Cls token initialization and frequency-specific mask token initialization that achieves fine-grained modeling of different frequency band information. Additionally, to evaluate FMs generalizability, we construct the Yellow River dataset, a large-scale multi-temporal collection that introduces challenging cross-domain tasks and establishes more rigorous standards for FMs assessment. Extensive experiments across six downstream tasks demonstrate Alliance's superior performance.

Purpose This study aims to examine the causal relationship between financial development (FD) and economic growth (EG) in 20 developing countries, evaluating the dynamics across multiple time and frequency scales to determine the validity of four key hypotheses: supply-leading, demand-following, feedback and neutrality. Design/methodology/approach Using monthly data from January 2000 to July 2023, this study uses wavelet transformation (WT) to decompose FD and EG series into multiple scales. A total of 18,000 causality and correlation tests are conducted to explore the interactions between FD and EG under various temporal and frequency contexts. Findings Results show that 40% of the tests at the level, short-run and business cycle scales reveal no causality, aligning with the neutrality hypothesis. However, in the long run, 60% of cases display strong bidirectional causality, supporting the feedback hypothesis. This indicates a reinforcing relationship between FD and EG, with frequency scales exerting a stronger influence on causality than time scales. Research limitations/implications This study focuses on quarterly lag structures because of computational constraints, excluding monthly lags that could offer more granular insights. Future research may expand the frequency decomposition or consider alternative econometric methods to validate the findings. Practical implications Policymakers in developing countries should design frequency-sensitive and long-term financial strategies. Strengthening financial institutions, improving regulatory mechanisms and promoting system-wide financial stability are crucial for reinforcing the mutually beneficial relationship between FD and EG. Originality/value This study contributes to the finance–growth literature by incorporating both time and frequency domain analyses using a large-scale data set. It challenges the conventional reliance on time-based causality frameworks and offers new insights into the scale-dependent nature of FD–EG linkages in developing economies.

Abstract Body-centric and body-worn applications have gained much more attention due to the emergence of wearable electronics. Antenna designs suitable for body-centric communications have then become a crucial part of any wearable system. This article introduces an ultra-wideband (UWB) antenna with dimensions of 24 × 18 × 0.8 mm 3 , designed using a flexible jean substrate. Using a slotted patch and defected ground, a wide impedance bandwidth of 15 GHz was achieved. A novel multiple input multiple output (MIMO) configuration with extended swastika-shaped connected ground is proposed to improve the reliability in body-centric communication. Frequency selective surface (FSS) is deployed to reduce specific absorption rate (SAR) and also to achieve directional radiation pattern at a specified frequency range to support both on- and off-body communications. A novel approach of corner-connected inter-rotated square rings was used to achieve wideband response. With the proposed FSS, the antenna renders a good peak gain of 9.1 dB, with the efficiency ranging from 72% to 94% in the UWB spectrum. The FSS proposal also helped in bringing down the SAR within the limits. All the MIMO diversity parameters reported remain good enough, ensuring link reliability. Real-time on-body measurements were carried out at various body parts. The path loss obtained while using the proposed antenna is considerably minimal. Satisfactory results were obtained from the time domain analysis carried out, which ensures good pulse similarity and minimum phase variations.

This study addresses the issue of traditional surface acoustic wave (SAW) tag failure under high-temperature conditions by proposing a SAW tag based on a multilayer structure of SiO2/Pt/128°YX-LiNbO3. The structure has been simulated using the finite element method/wave-number domain analysis (FEM/WDA) approach, which reveal the effects of reflector topological parameters on the scattering characteristics of SAWs. Compared with Pt/128°YX-LiNbO3, the bulk wave scattering in the multilayer structure is reduced by 50%. In the micro-nanofabrication of the tag, a low-roughness, high-density SiO2 film is prepared using physical vapor deposition (PVD). Test results indicate that the tag exhibits a temperature coefficient of frequency (TCF) of −32.38 ppm/°C over a wide temperature range of 30–600°C. After undergoing thermal shock at 600 °C for 336 h, the time-domain reflection amplitude decreases by less than 1%, demonstrating that the SiO2 protective layer effectively suppresses the high-temperature decomposition of LiNbO3 and reduces the agglomeration rate of Pt electrodes. Experimental results confirm that the proposed high-temperature-resistant SAW tag maintains stable performance under prolonged exposure to 600 °C environments. The tag has been installed on the surface of a steel ladle in a steel plant, demonstrating excellent reliability in a vacuum degassing environment.