Optimal Frequency Research Articles

Research on the Prediction of Optimal Frequency for Vibration Mixing and Comparison on Initial Performance of Cold-Recycled Asphalt Emulsion Mixture.

The multicomponent cold-recycled asphalt emulsion mixture (CRAEM) has the ability of antireflection cracking between the base and the bottom surface layer, but it has secondary compaction and residual void, which is not conducive to crack resistance and fatigue performance. The application of high-frequency vibration mixing technology can reduce voids and improve crack resistance, but it is limited by the complexity of testing to determine the optimal mixing frequency. The fractal dimension of gradation is deduced by fractal theory, and the prediction model for optimal frequency is proposed. Dry, wet, freeze-thaw splitting tests, and rutting tests were employed to test the early mechanical properties of high-frequency vibration mixing specimens corresponding to different vibration accelerations, and mercury inclusion tests were utilized to compare the void distribution corresponding to the optimal mixing frequency and forced mixing, and to verify the prediction model for optimal frequency. The results indicate that the high-frequency vibration mixing technology is able to benefit the initial cracking resistance (28.1% increase), moisture stability (11.2% increase), and high-temperature stability on the macro level on the optimal frequency. Meanwhile, the void distribution structure can be optimized, reducing the proportion of harmful voids and increasing the proportion of transitional pores on the micro level. However, the freeze-thaw resistance needs to be further studied. This study reduces the number and cost of experiments to determine the optimal frequency, and provides theoretical guidance and technical support for the engineering application of the CRAEM.

An efficient acoustic energy harvester by using deep learning-based traffic prediction

With rapid urbanisation, traffic noise pollution has become a growing community concern. However, traffic noise can be valuable due to its ubiquity and cleanliness, which can be exploited for acoustic energy harvesting systems. This paper aims to develop an efficient acoustic energy harvester to improve energy utilisation efficiency and reduce noise levels by using deep-learning methods. We propose a temporal graph attention convolutional network (TGACN) to predict noise frequency, providing guidance for the acoustic energy harvester design. Power efficiency can be maximised by adjusting the harvester's neck size to the frequency predicted by the TGACN model. The relationship between neck size and the optimum frequency range is obtained through simulations and experiments. We analysed the performance differences among the various cavities in multiple-cavity acoustic energy harvesters and discussed the circuit connection methods between each cavity to identify the optimal configurations. Additionally, we evaluated the superiority of TGACN over the state-of-the-art methods in terms of prediction accuracy by conducting extensive experiments with two real-world datasets.

Magnetic Field Mapping-Based Battery Management System (BMS) for Lead Acid Application

Lead Acid Batteries (LAB) are a widely used technology in Energy Storage Systems (ESS) due to their abundant and low-cost materials, non-flammable water-based electrolyte, and high recyclability rate. The main applications for LABs are in mobility, data centers, and telecommunications. According to the Consortium of Battery Innovation (CBI), the market for lead acid batteries is projected to increase 150 GWh between 2025 and 2030. Therefore, it is fundamental to identify emerging failure modes in LABs in ESS systems to reduce battery degradation and to extend the use life.An important failure mode in flooded LABs is acid stratification. This research explores H+ concentration changes in H2SO4 (sulfuric acid) electrolyte as a correlation of acid stratification. The objective is to develop a non-invasive, non-destructive, and accurate real-time battery monitoring system. The Magnetic Field Probing (MFP) technique, popular in the medical imaging field, shows a promising solution for ESS performance evaluation. This technique can measure induced magnetic field changes based on variation in H+ concentration in the cell during cycling. See Figure. 1 (a). MFP provides early onset notification of stratification in flooded LABs with capability to discern other failure modes in non-flooded lead batteries.A set of experimental trials measured pH and magnetic field variation of H2SO4 electrolyte. See Figure. 1 (a). The preliminary results demonstrated changes in electrolyte pH due to possible stratification. See Figure. 1 (b). A correlation between the output voltage resulting from the interaction of the induced magnetic field and the changes in the electrolyte concentration was found. See Figure 1(c) The optimal AC input frequency maximizes the induced magnetic field response. The value found for optimal AC input frequency was 32kHz, which depends on the physical properties of the magnetic field inductors (air core solenoid coils). The experimental results showed and concluded that the induced magnetic field across the tested cell is also inversely proportional to the distance between coils attached to the cell.Further research involves the use of magneto-resistors to establish a magnetic field mapping in lead acid cells as they are being cycled. Magnetic field response is measured while lead acid cells are cycled from 100% to 20% state of charge range. In the future, this study will provide insights and findings to model and predict cell stratification in lead acid cells with electrochemical computational simulations.Keywords: Energy storage systems, lead acid batteries, acid stratification, magnetic field, non-invasive method Figure 1

Advances in Millimeter-Wave Treatment and Its Biological Effects Development.

This comprehensive review critically examines the current state of research on the biological effects of millimeter-wave (MMW) therapy and its potential implications for disease treatment. By investigating both the thermal and non-thermal impacts of MMWs, we elucidate cellular-level alterations, including changes in ion channels and signaling pathways. Our analysis encompasses MMW's therapeutic prospects in oncology, such as inducing apoptosis, managing pain, and modulating immunity through cytokine regulation and immune cell activation. By employing a rigorous methodology involving an extensive database search and stringent inclusion criteria, we emphasize the need for standardized protocols to enhance the reliability of future research. Although MMWs exhibit promising therapeutic potential, our findings highlight the urgent need for further elucidation of non-thermal mechanisms and rigorous safety assessments, considering the intricate nature of MMW interactions and inconsistent study outcomes. This review underscores the importance of focused research on the biological mechanisms of MMWs and the identification of optimal frequencies to fully harness their therapeutic capabilities. However, we acknowledge the challenges of variable study quality and the necessity for advanced quality control measures to ensure the reproducibility and comparability of future investigations. In conclusion, while MMW therapy holds promise as a novel therapeutic modality, further research is imperative to unravel its complex biological effects, establish safety profiles, and optimize treatment protocols before widespread clinical application.

Strengthening effect of mechanical vibration on the carbonation properties of steel slag compact

This study examines the impact of mechanical vibration on the carbonation process of steel slag, a substantial byproduct of steel production. Employing a methodical experimental design that modulates mechanical vibration frequencies, the research reveals that mechanical vibration markedly influences carbonation, with an optimal frequency of 180 Hz leading to a 27.5 % increase in compressive strength of steel slag compact and an increase in CO2 uptake of 6.8 %. The application of vibration appears to catalyze the conversion of calcium-rich minerals into well-formed rhombohedral calcite, which emerges as the predominant carbonation product. Mechanism analysis suggests that vibration-induced particle redistribution creates new reaction surfaces, thereby accelerating the carbonation process. However, the effect is more pronounced in early stages, with diminishing returns as carbonation progresses. These pivotal findings propose an innovative strategy to augment the efficiency of carbonation and the inherent material properties of steel slag, thereby fostering a more sustainable approach to the utilization of industrial byproducts.

Collaborative modelling of gas-solid reacting flow in a fuel reactor equipped with process controllers in chemical looping combustion/conversion

Chemical reactors usually feature complex internal reacting flows, and they are usually equipped with process controllers to stabilise their operations, especially for highly dynamic reactors including chemical looping combustion/conversion (CLC). However, their dynamic internal states are not well understood due to the lack of reliable research tools. In this work, for the first time, an innovative numerical collaborative model is developed to describe the reacting flow details and simulate the response to a process controller. The collaborative model is applied to a fuel reactor (FR) in a CLC to demonstrate its effectiveness. The detailed internal flow patterns in the FR are obtained and described by the three-phase reacting flow CFD model, and the circulating rate of oxygen carrier (OC) is controlled and optimised by the fuzzy logic controller (FLC). The controller is designed to operate at varying control time intervals, 1 s, 2 s, and 5 s, to study the optimal frequency for data sampling and feedback. The efficiency of the controller to the FR is tested in terms of OC circulation, gas components and the general performance of the reactor. The results show that the stability of OC circulating rate and performance are effectively improved by the process controller; among the three control intervals, the 2 s interval shows the highest feasibility and capability of maintaining stable OC circulation and CO2 yield in the FR. Quantitatively, compared to the base case without a controller, the range of OC circulating rates has been narrowed from [0.014, 0.534] to [0.018, 0.385] in control case A, [0.075, 0.342] in control case B, and [0.004, 0.530] in control case C. Meanwhile, the CO2 yield increased from 86.93 % in the base case to 87.98 % in control case A, 88.16 % in control case B, but decreased to 85.4 % in control case C due to poor controlling performance. The combustion efficiency increased from 84.20 % in the base case to 84.87 % in control case A, 86.22 % in control case B, and 85.88 % in control case C. Among all control cases, control case B presents the narrowest data range and highest efficiency, indicating optimal control performance in improving OC circulation stability. The deviation of OC circulating rate values decreases from 0.123 to 0.058, and the CO2 yield increases from 86.93 % to 88.16 % in control case B with a 2 s interval. This work provides a new cost-effective tool for real-time simulating and controlling the reacting flow system including CLC for clean combustion and solid wastes conversion.

Statistical design for health monitoring in laboratory animal facilities using sentinel animals.

Regular health monitoring is crucial in laboratory animal facilities to determine the presence or absence of specific pathogens. One common approach to monitoring involves the use of sentinel animals, which are periodically exposed to biological material from the cages being monitored. At a certain point, some of these sentinel animals are tested for pathogens. This article discusses designing an effective sampling scheme to meet desired quality standards. It addresses questions such as the number of sentinel animals required, the frequency of sampling biological material, the selection of cages based on facility set-up, and the optimal frequency and quantity of sentinel animal tests. While existing design formulas are available for simple random sampling, no quantitative recommendation exists for using sentinel animals to the best of our knowledge. We propose a Monte Carlo simulation-based approach in this article to address this. Our algorithm has been implemented in a publicly accessible web page at http://nolan.cnb.csic.es/sentinelcagesmanager.

Assessment of UHF Frequency Range for Failure Classification in Power Transformers.

Ultrahigh-frequency (UHF) sensing is one of the most promising techniques for assessing the quality of power transformer insulation systems due to its capability to identify failures like partial discharges (PDs) by detecting the emitted UHF signals. However, there are still uncertainties regarding the frequency range that should be evaluated in measurements. For example, most publications have stated that UHF emissions range up to 3 GHz. However, a Cigré brochure revealed that the optimal spectrum is between 100 MHz and 1 GHz, and more recently, a study indicated that the optimal frequency range is between 400 MHz and 900 MHz. Since different faults require different maintenance actions, both science and industry have been developing systems that allow for failure-type identification. Hence, it is important to note that bandwidth reduction may impair classification systems, especially those that are frequency-based. This article combines three operational conditions of a power transformer (healthy state, electric arc failure, and partial discharges on bushing) with three different self-organized maps to carry out failure classification: the chromatic technique (CT), principal component analysis (PCA), and the shape analysis clustering technique (SACT). For each case, the frequency content of UHF signals was selected at three frequency bands: the full spectrum, Cigré brochure range, and between 400 MHz and 900 MHz. Therefore, the contributions of this work are to assess how spectrum band limitation may alter failure classification and to evaluate the effectiveness of signal processing methodologies based on the frequency content of UHF signals. Additionally, an advantage of this work is that it does not rely on training as is the case for some machine learning-based methods. The results indicate that the reduced frequency range was not a limiting factor for classifying the state of the operation condition of the power transformer. Therefore, there is the possibility of using lower frequency ranges, such as from 400 MHz to 900 MHz, contributing to the development of less costly data acquisition systems. Additionally, PCA was found to be the most promising technique despite the reduction in frequency band information.

Low-high-low doped Ga2O3 Schottky barrier IMPATT diodes on various crystal orientations for terahertz applications

This article investigates low-high-low doped Ga2O3 Schottky barrier IMPATT diodes based on [100], [010], and [001] crystal orientations to promote oscillation power at the low-frequency band of terahertz regime. Simulation results demonstrate a conversion power of 4.88 MW/cm2 along [100] at its optimum frequency of 100 GHz, increasing by 0.73 times and 2.2 times compared to the [100] at 150 GHz and the [001] at 100 GHz, and exhibit the highest conversion efficiency of 6.83 % along [010] as indicated by the DC-RF conversion ability promoting 37 % and 30 % than that of [100] and [001] orientations, respectively. When considering a parasitic resistance of 1 × 10−5 Ω cm2, the optimal frequencies of along three orientations decrease to 90 GHz, 127 GHz, and 91 GHz, correspondingly, yielding the peak output power and total efficiency decrease by 0.3–0.4 times compared to those of the conversion characteristics.

Improving Recovery of Diatoms Bio-Silica Using Chemical Treatment with VAUS TM.

High-temperature baking is a typical method to remove organic matter from diatoms, but it's not suitable for bio-silica because of the high crystallinity. This study provides a method using the VAUS™ to remove organic matter from diatoms more quickly and biocompatibly. Organic matter was removed by using VAUS™, while NaOCl was utilized to remove the organic matter from the silicate frustule. The optimal frequency for organic matter removal was investigated to domestically produced M. nummuloides. The removal efficiency of TOC/TN was calculated and analyzed. The C and Si elements were analyzed in EDS, while visual confirmation of organic matter removal was analyzed by using XRD. TOC RE% at a frequency of 35kHz exhibited the highest value, indicating a statistically significant difference. XRD analysis demonstrated that the organic matter was almost removed using NaOCl compared to the high-temperature baked M. nummuloides. In the EDS analysis, there were significant differences in the C and Si elements with respect to frequency. This is very similar to the values from the positive control group, high-temperature baked M. nummuloides. This new procedure of applying periodic negative pressure to NaOCl pre-treatment is considered to be an effective method of chemically removing organic matter from diatoms.

Impact of dust and temperature on photovoltaic panel performance: A model-based approach to determine optimal cleaning frequency

Enhancing the reliability of photovoltaic (PV) systems is of paramount importance, given their expanding role in sustainable energy production, carbon emissions reduction, and supporting industrial growth. However, PV panels commonly encounter issues that significantly impact their performance. Specifically, the accumulation of dust and the rise in internal temperature lead to a drop in energy production efficiency. The primary issue addressed in this paper is using mathematical modeling to determine the optimal cleaning frequency. This paper first focuses on stochastic modeling for dust accumulation and temperature changes in PV panels, considering varying environmental conditions and proposing a model-based approach to determine the optimal cleaning frequency. Dust accumulation is described using a Non-homogeneous compound Poisson process (NHCPP), while temperature evolution is modeled using Markov chains. Within this framework, we consider the impact of wind speed and rainfall on dust accumulation and temperature. These factors, treated as covariates, are modeled using a two-dimensional time-continuous Markov chain with a finite state space. A Condition-based cleaning policy is proposed and assessed based on the degradation model. Optimal preventive cleaning thresholds and cleaning frequency (periodic and non-periodic) are determined to minimize the long-term average maintenance cost. The gain achieved by non-periodic inspections compared to periodic inspections ranges from 3.83% to 9.37%. Numerical experiments demonstrate the performance of the proposed cleaning policy, highlighting its potential to improve PV system efficiency and reliability.

ESTIMATION OF THE AVERAGE GRAIN SIZES IN METAL AND THEIR DISPERSION BY CHANGING THE AMPLITUDES OF BOTTOM ECHOES OF LONGITUDINAL WAVES WITH A DIFFERENT NUMBER OF REFLECTIONS

A model (algorithm) has been developed for estimating the average size of a metal grain and the spread of its magnitude (standard deviation) according to experimental values of attenuation coefficients due to scattering of longitudinal waves on grains determined by the ratio of the amplitudes of bottom echoes (of the first and higher order) at three frequencies using a table of values of the difference between experimental and calculated values of attenuation coefficients. Computer modeling of the pulse amplitude differences of the 1st and 3rd bottom echoes for normal probe models with nominal frequencies of 5.0, 7.5 and 10.0 MHz and two variants of grain parameters used to test the specified algorithm has been carried out. The results obtained make it possible to choose the optimal operating frequency for metal control by a normal probe based on the estimated value of attenuation of longitudinal waves, determined by the average size of metal grains and the value of its magnitude spread.

Frequency tunable non-invasive microwave blood glucose sensor based on negative impedance circuit

Abstract This article proposes a frequency adjustable non-invasive blood glucose microwave sensor based on negative impedance circuit. Firstly, this article establishes a skin-blood-skin human tissue model for the detection of non-invasive microwave sensors. Compared to traditional passive microwave blood glucose sensors, this design uses a negative impedance compensation circuit (NIC), greatly increasing microwave penetration and improving its detection sensitivity. In addition, this design achieves the goal of adjustable operating frequency by adding an additional dielectric layer at the detection end. This function is of great significance for microwave non-invasive blood glucose sensors. On the one hand, it can adjust to the optimal operating frequency band according to the characteristics of blood glucose changes; On the other hand, it is possible to measure changes in blood glucose concentration at multiple frequency points, thereby increasing the accuracy of detection. The relevant results show that adjusting the frequency band can reach 1–4 GHz, and the specific method of frequency adjustment is provided in the article; Meanwhile, four initial frequency points were selected in the article, at which the results show that the sensitivity reaches 1.356 × 10−3, 3.142 × 10−3, 2.071 × 10−3, 1.903 × 10−3. The above results demonstrate the superiority of our design, and blood glucose sensors based on this principle are expected to break through the limitations of current blood glucose sensors.

An enhanced model based on deep operator network for very short-term forecasting of ship motion

Very short-term forecasting of ship motion involves forecasting future ship movements based on learned characteristics from historical motion data. However, ship motion exhibits not only temporal features but also features in the frequency domain, which are often overlooked. This paper introduces a novel method called Fourier Transform and Multilayer Perceptron-net enhancement based on Deep Operator Network (DeepONet), abbreviated as FMD. This approach effectively captures and learns ship motion patterns in both the temporal and frequency domains. Specifically, the branch net of DeepONet learns temporal features, while the trunk net performs Fourier filtering to capture the underlying ship motion patterns. In addition, the learning effectiveness of Fourier filtering is complemented by using MPL-net to enhance the extraction of detailed features in motion data. To evaluate the prediction performance of FMD, this study explores the optimal filtering frequency of the FMD model using experimental ship model motion data. Comparative testing with the DeepONet model includes multi-step prediction, coupled data forecasting, and generalization studies. Testing results demonstrate that the proposed FMD model improves prediction accuracy from 11.78% to 33.10% under Mean Squared Error (MSE) compared to the DeepONet model. Even under sea conditions ranging from mild to intense, the FMD model maintains high accuracy, with an improvement of over 30% in accuracy compared to DeepONet at longer step lengths under MSE conditions. Testing results indicate the superiority and advancement of FMD in prediction accuracy, generalization, and versatility, showcasing significant advantages in very short-term forecasting of ship motion.

Optimizing Bodybuilding Training Frequency for Weight Loss and Psychological Well-Being in Overweight Individuals: A 12-Week Study

Background: The increasing incidence of overweight and obesity raises a global public health challenge, with overweight individuals (BMI 25–29.9) at higher risk for chronic diseases like cardiovascular conditions and diabetes (WHO, 2024). While lifestyle changes, including dieting and exercise, are key strategies for weight management, bodybuilding is gaining attention for its potential in fat loss and improving body composition. However, the optimal frequency for bodybuilding sessions in overweight individuals remains unclear. This study explores the effects of different training frequencies on body weight, BMI, and fat loss. Methods: This cross-sectional study involved 90 participants, all classified as overweight (BMI 25–29.9). Participants were recruited through online advertisements and were divided into three groups, each following a 12-week bodybuilding training regimen with varying frequencies: twice, thrice, or five times weekly. Baseline body weight and BMI were recorded before training, and changes were assessed at the end of the program. A short questionnaire evaluated the effects of the training on weight loss, body composition, and psychological outcomes. Results: This study involved 90 participants, primarily aged 25-34 (48.9%). The demographic analysis revealed that males constituted 66.7% of the sample, exhibiting a mean weight loss of 75.23 pounds (p < 0.001) after 12 weeks of bodybuilding training, effectively rejecting the hypothesis that bodybuilding training has no significant impact on weight loss. Additionally, while no significant differences in weight loss were observed based on training frequency (F(2, 87) = 0.791, p = 0.457), both weight loss and body mass index (BMI) significantly improved with training. Conclusion: Overall, regular bodybuilding training positively influenced weight loss, body composition, and psychological well-being.

Connectivity study on resting-state EEG between motor imagery BCI-literate and BCI-illiterate groups.

Although Motor Imagery-based Brain-Computer Interface (MI-BCI) holds significant potential, its practical application faces challenges such as BCI-illiteracy. To mitigate this issue, researchers have attempted to predict BCI-illiteracy by using the resting state, as this was found to be associated with BCI performance. As connectivity's significance in neuroscience has grown, BCI researchers have applied connectivity to it. However, the issues of connectivity have not been considered fully. First, although various connectivity metrics exist, only some have been used to predict BCI-illiteracy. This is problematic because each metric has a distinct hypothesis and perspective to estimate connectivity, resulting in different outcomes according to the metric. Second, the frequency range affects the connectivity estimation. In addition, it is still unknown whether each metric has its own optimal frequency range. Third, the way that estimating connectivity may vary depending upon the dataset has not been investigated. Meanwhile, we still do not know a great deal about how the resting state EEG network differs between BCI-literacy and -illiteracy. To address the issues above, we analysed three large public EEG datasets using three functional connectivity (FC) and three effective connectivity (EC) metrics by employing diverse graph theory measures. Our analysis revealed that the appropriate frequency range to predict BCI-illiteracy varies depending upon the metric. The alpha range was found to be suitable for the metrics of the frequency domain, while alpha + theta were found to be appropriate for Multivariate Granger Causality (MVGC). The difference in network efficiency between BCI-literate and -illiterate groups was constant regardless of the metrics and datasets used. Although we observed that BCI-literacy had stronger connectivity, no other significant constructional differences were found. Based upon our findings, we predicted MI-BCI performance for the entire dataset. We discovered that combining several graph features could improve the prediction's accuracy.

Novel scale for evaluating the therapeutic efficacy of per-oral endoscopic myotomy in achalasia.

Symptom scales for achalasia after per-oral endoscopic myotomy (POEM) are lacking. This study aimed to propose a new scale based on the conventional Eckardt score (c-ES) and evaluate persistent symptoms that impair patients' quality of life (QOL) post-POEM. Dysphagia, regurgitation, and chest pain frequencies were assessed using a 6-point scale modified-ES (m-ES) after POEM, with "occasional" symptoms on the c-ES further subdivided into three-period categories on m-ES. Symptom severity was further evaluated using a 5-point scale ranging from 1 to 5 points, with a score ≥ 3 points defined as persistent symptoms impairing QOL. We analyzed the correlation between the m-ES and severity score, diagnostic performance of the m-ES for persistent symptoms, and overlaps between each residual symptom. Overall, 536 patients (median follow-up period, 2.9years) post-POEM were included in this multicenter study. Significant correlations were observed between the m-ES and severity scores for dysphagia (r = 0.67, p < 0.01), regurgitation (r = 0.73, p < 0.01), and chest pain (r = 0.85, p < 0.01). Twenty-six patients (4.9%) had persistent symptoms post-POEM, and 23 of them had m-ES-specific symptom frequency ≥ once a month, which was determined as the optimal frequency threshold for screening persistent symptoms. The total m-ES predicted persistent symptoms more accurately than the total c-ES (area under the curve: 0.95 vs. 0.79, p < 0.01). Furthermore, dysphagia and chest pain were the major residual symptoms post-POEM covering 91.4% of regurgitation. The new post-POEM scale successfully evaluated the QOL-based patient symptom severities. Our study implied the possibility of a simpler scale using residual dysphagia and chest pain.

Application of foot massage therapy to reduce pain scale of elderly with rheumatoid arthritis

Rheumatoid Arthritis (RA) is a chronic autoimmune disorder characterized by systemic inflammation, joint damage, and persistent pain, particularly in older adults. The debilitating nature of RA significantly impairs individuals' quality of life, leading to diminished independence and increased reliance on pharmacological interventions. While pharmaceutical treatments play a crucial role in managing RA symptoms, complementary and alternative therapies, such as massage, have garnered attention for their potential to alleviate pain and improve overall well-being. This study adopts a descriptive approach to examine the efficacy of foot massage as an intervention for RA-related pain in older adults. Two participants diagnosed with RA were selected for the case study. Family nursing care principles were integrated into the intervention to provide holistic support. Pain scale assessments were conducted before and after each foot massage session to measure changes in pain intensity. Analysis of the data revealed a significant reduction in pain intensity among participants following the foot massage sessions. Pain levels decreased from moderate to mild, indicating the efficacy of foot massage in alleviating RA-associated pain in older adults.The findings of this study support the notion that foot massage can serve as a viable intervention for managing RA-related pain in older adults. The mechanisms underlying the efficacy of foot massage, including its ability to stimulate vasodilation and enhance endorphin production, highlight its potential therapeutic benefits for individuals grappling with RA. Foot massage emerges as a promising adjunctive therapy for alleviating pain and improving the well-being of older adults living with Rheumatoid Arthritis. Integrating foot massage into holistic care approaches can enhance pain management strategies and contribute to the overall quality of life for individuals affected by this debilitating condition. Further research is warranted to explore the long-term effects and optimal frequency of foot massage in RA management.

Spatial Localization of Transformer Inspection Robot Based on Adaptive Denoising and SCOT-β Generalized Cross-Correlation.

In the detection process of the internal defects of large oil-immersed transformers, due to the huge size of large transformers and metal-enclosed structures, the positional localization of miniature inspection robots inside the transformer faces great difficulties. To address this problem, this paper proposes a three-dimensional positional localization method based on adaptive denoising and the SCOT weighting function with the addition of the exponent β (SCOT-β) generalized cross-correlation for L-type ultrasonic arrays of transformer internal inspection robots. Aiming at the strong noise interference in the field, the original signal is decomposed by an improved Empirical Mode Decomposition (EMD) method, and the optimal center frequency and bandwidth of each mode are adaptively searched. By extracting the modes in the frequency band of the positional localization signal, suppressing the modes in the noise frequency band, and reconstructing the Intrinsic Mode Function (IMF) of the independently selected superior modal components, a signal with a high signal-to-noise ratio is obtained. In addition, for the traditional mutual correlation algorithm with a large delay estimation error at a low signal-to-noise ratio, this paper adopts an improved generalized joint weighting function, SCOT-β, which improves the anti-jamming ability of the generalized mutual correlation method at a low signal-to-noise ratio by adding an exponential function to the denominator term of the SCOT weighting function's generalized cross-correlation. Finally, the accurate positional localization of the transformer internal inspection robot is realized based on the quadratic L-array and search-based maximum likelihood estimation method. Simulation and experimental results show the following: the improved EMD denoising method better improves the signal-to-noise ratio of the positional localization signal with a lower distortion rate; in the transformer test tank, which is 120 cm in length, 100 cm in width, and 100 cm in height, based on the positional localization method in this paper, the average relative positional localization error of the transformer internal inspection robot in three-dimensional space is 2.27%, and the maximum positional localization error is less than 2 cm, which meets the requirements of engineering positional localization.

Impact of radiological follow-up frequency on resected lung cancer: a propensity score matching analysis.

Despite advances in lung cancer treatment and the subsequent improvement in oncological outcomes, the optimal frequency of radiological follow-up remains unclear. Current recommendations lack consensus and do not consider individual patient characteristics and tumor factors. This study aimed to examine the impact of radiological follow-up frequency on oncological outcomes following lung cancer resection. A prospective multicenter study, involving patients who underwent anatomical lung resection in the GEVATS database between December 2016 and March 2018. The relationship between surveillance frequency and oncological outcomes was evaluated. Two groups were established based on follow-up frequency: low frequency (LF) and high frequency (HF). Subgroup analyses were performed based on tumor stage, histology, lymphadenectomy, and adjuvant therapy. Propensity score matching (PSM) was applied to balance the groups. A total of 1,916 patients were included in the study, LF 444 (23.17%), HF 1,472 (76.83%). Factors associated with HF surveillance included higher stage, adjuvant chemotherapy and adjuvant radiotherapy. Subanalyses were performed after PSM for various factors, revealing significant differences between LF and HF groups in cancer-specific survival among who received adjuvant therapy {LF 53.021 months [95% confidence interval (CI): 48.622-57.421] vs. HF 58.836 months (95% CI: 55.343-62.330); HR 0.453, 95% CI: 0.242-0.849; P=0.013}, as well as overall survival for patients with squamous cell carcinoma [LF 54.394 months (95% CI: 51.424-57.364) vs. HF 61.578 months (95% CI: 59.091-64.065); HR 0.491, 95% CI: 0.299-0.806; P=0.005] and those who received adjuvant therapy LF 50.176 months [95% CI: 45.609-54.742) vs. HF 57.189 months (95% CI: 53.599-60.778); HR 0.503, 95% CI: 0.293-0.865; P=0.013]. Findings suggest that high-frequency surveillance only improves survival outcomes in lung cancer patients who received adjuvant treatment or had squamous cell carcinoma. Therefore, future guidelines for lung cancer follow-up should consider individualizing the frequency of radiological surveillance based on patients' risk profiles.