Modal Method Research Articles

Energy-Efficient Passivity-Based Sliding Mode Controller for Small-Scale Quadrotor UAV for Trajectory Tracking

Quadrotor unmanned aerial vehicles (UAVs) have emerged as versatile platforms applicable to various fields such as surveillance, reconnaissance, and mapping. However, the limited energy capacity inherent in these vehicles poses a significant challenge. This limitation is a critical concern hindering their widespread adoption across diverse applications. Despite various proposed technological solutions addressing the energy consumption issue, the central challenge for the control community lies in developing controllers that ensure stability, robustness, and energy efficiency. In this paper, we present a passivity-based sliding mode controller (P-SMC) designed specifically for quadrotor UAVs. The suggested controller capitalizes on the robustness of the SMC and the energy efficiency of passivity-based control theory. Feedback passification is employed to create a sliding surface with passivity, ensuring stability. The inclusion of a noncontinuity term in the proposed P-SMC guarantees global asymptotic convergence to the sliding surface. To address the multi-faceted nature of the problem, a multi-objective optimization criterion is introduced. This criterion, which combines tracking error and control energy, is solved using the ant colony optimization (ACO) algorithm. The optimization process aims to identify control parameters that strike a balance between tracking accuracy and energy efficiency. Additionally, a conventional sliding mode method is developed to respond to conditions of attempted reach and sliding. Finally, the effectiveness of the proposed method is demonstrated through simulation results considering piecewise constant external disturbances. The outcomes based on the proposed control strategy indicate a notable reduction in energy consumption (ranging from [Formula: see text] to [Formula: see text]), faster reaching times (5–8 times), higher accuracy approximately ([Formula: see text]), and reduced chattering compared to the conventional sliding mode technique.

A Mendelian randomization study of the association between serum uric acid and osteoporosis risk.

The relationship between serum uric acid (SUA) and osteoporosis (OP) has yielded conflicting results in observational studies. This Mendelian randomization (MR) study aims to elucidate the causal association between SUA and OP. A two-sample MR analysis was conducted using summary-level data from genome-wide association studies (GWAS). Two sets of polygenic instruments strongly associated (p < 5 × 10-8) with SUA were extracted from the CKDGen consortium and UK biobank. Polygenic instruments associated with OP (p < 5 × 10-8) were derived from FinnGen biobank and UK biobank. Inverse variance weighting (IVW) was employed as the primary analysis method. Additionally, we utilized MR-Egger, weighted median, the simple mode method, and the weighted mode as complementary analyses. Cochran's Q statistics were used to assess heterogeneity, with MR-Egger intercept testing and MR pleiotropy residual sum and outlier (MR-PRESSO) to examine horizontal pleiotropy. Sensitivity analysis was performed using the leave-one-out method. The IVW analysis conducted across four groups confirms no significant causal relationship between SUA concentration and OP: UKB-UKB (OR: 1.001, 95% CI: 0.999-1.003, p=0.464), CKD-UKB (OR: 1.001, 95% CI: 0.999-1.003, p=0.349), UKB-Fin (OR: 0.934, 95% CI: 0.747-1.168, p=0.549), CKD-Fin (OR: 1.041, 95%CI: 0.934-1.161, p=0.470). Furthermore, additional four MR analyses corroborated these findings. Upon excluding all outliers identified by the MR-PRESSO test, no significant directional pleiotropy was observed, except for some data heterogeneity noted in the UKB-UKB group (Q=50.65, P=0.002). Additionally, a leave-one-out analysis indicated that no single SNP exerted undue influence on the results. This MR analysis provides convincing genetic evidence that there is no causal association between SUA and OP, SUA is unlikely to increase or reduce the risk of OP.

Circulating levels of cytokines and risk of urologic cancers: a two-sample Mendelian randomization study

BackgroundChronic inflammation is associated with the etiology of various cancers. However, there is a lack of systematic research in urologic cancers. This study aims to use a two-sample Mendelian randomization (MR) approach to evaluate the role of circulating cytokines in the development of urologic cancers.MethodsWe obtained the summary-level data for bladder cancer (373,295 cases and 372,016 controls), prostate cancer (462,933 cases and 459,664 controls), and kidney cancer (463,010 cases and 461,896 controls) from the UK Biobank. Genetic variations linked to 41 circulating cytokines were used as instrumental variables (IVs) in meta-analyses of genome-wide association studies (GWASs) involving 8,293 individuals from Finland. We primarily used the inverse-variance weighted (IVW) method to assess the potential associations between the 41 cytokines and the risk of 3 common urologic cancers. Weighted-median method, weighted mode and simple-median method were used to assess the sensitivity. Heterogeneity and pleiotropic outlier were evaluated by Cochran's Q test and MR-Egger regression. Genetic correlation, colocalization analysis and multivariable MR analysis were used to further validate the potential pleiotropy.ResultsAfter the Bonferroni correction, there was an observed association between elevated genetically predicted levels of CCL27 and a heightened risk for bladder cancer. Conversely, IL-12p70 levels were found to have a protective association against the risk of bladder cancer. Sensitivity analyses utilizing various IV sets and MR approach remained robust. Furthermore, we found potential associations of 7 cytokines with urologic cancers (4.07 × 10−4 ≤ P < 0.05).ConclusionOur study supported causal associations between CCL27, IL-12p70 and bladder cancer risk and potential associations of 7 cytokines with the risk of urologic cancers, helping us to further understand the pathogenesis of urologic cancers and providing clues for improving diagnostic accuracy and therapies.

Dynamic Modeling and Analysis of Multi-Span Timoshenko Beams Under Moving Loads

Dynamic response analysis of multi-span beams is one of the important topics in the field of highway and railway engineering, which provides valuable guidance for the design, operation and maintenance of multi-span bridges. It is worth considering how to calculate the dynamic characteristics of multi-span beams quickly and conveniently. In this study, an effective analytical method is developed for the dynamic modeling and investigations of multi-span Timoshenko beams under moving loads by employing the assumed mode method (AMM). Based on Hamilton’s principle, the equation of motion of the Timoshenko multi-span beam subjected to moving loads is formulated and the natural frequencies are calculated. A comprehensive investigation is conducted on the dynamic responses of the multi-span Timoshenko beam considering different factors, including the length-to-thickness ratio, disorder degree, arrangement order, number of spans and related parameters of moving loads. In addition, the influence of the interval and velocity of the moving load series on the dynamic responses, including displacement, velocity and acceleration of the multi-span beam, are analyzed in detail. The theoretical calculation results of multi-span Timoshenko beams under moving loads are compared with numerical results obtained from ANSYS software, and the results are in good agreement. This demonstrates that the developed method, which utilizes the AMM to analyze the dynamic characteristics of multi-span beams subjected to moving loads, significantly simplifies the calculations and is well-suited for practical engineering applications.

An Adaptive Chirp Mode Decomposition-Based Method for Modal Identification of Time-Varying Structures

Modal parameters are inherent characteristics of civil structures. Due to the effect of environmental factors and ambient loads, the physical and modal characteristics of a structure tend to change over time. Therefore, the effective identification of time-varying modal parameters has become an essential topic. In this study, an instantaneous modal identification method based on an adaptive chirp mode decomposition (ACMD) technique was proposed. The ACMD technique is highly adaptable and can accurately estimate the instantaneous frequencies of a structure. However, it is important to highlight that an initial frequency value must be selected beforehand in ACMD. If the initial frequency is set incorrectly, the resulting instantaneous frequencies may lack accuracy. To address the aforementioned problem, the Welch power spectrum was initially developed to extract a high-resolution time–frequency distribution from the measured signals. Subsequently, the time–frequency ridge was identified based on the maximum energy position in the time–frequency distribution plot, with the frequencies associated with the time–frequency ridge serving as the initial frequencies. Based on the initial frequencies, the measured signals with multiple degrees of freedom could be decomposed into individual time-varying components with a single degree of freedom. Following that, the instantaneous frequencies of each time-varying component could be calculated directly. Subsequently, a sliding window principal component analysis (PCA) method was introduced to derive instantaneous mode shapes. Finally, vibration data collected under various operational scenarios were used to validate the proposed method. The results demonstrated the effective identification of time-varying modal parameters in real-world civil structures, without missing modes.

Causal relationship between oral microbiota and epilepsy risk: Evidence from Mendelian randomization analysis in East Asians.

Gut microbiota can traverse into the brain, activate the vagus nerve, and modulate immune responses and inflammatory processes, thereby influencing the onset of epileptic seizures. However, research on oral microbiota and epilepsy remains limited, and observational studies have been inconsistent. We aim to estimate the potential links between oral microbiota and epilepsy and elucidate which specific oral microbes may directly influence the pathogenesis of epilepsy. A two-sample MR analysis was conducted using genome-wide association study (GWAS) data specific to OM and epilepsy in East Asian individuals. Single nucleotide polymorphisms (SNPs) independent of confounders served as instrumental variables (IVs) to deduce causality. MR methodologies, including inverse variance weighted (IVW), MR-Egger, weighted median, and weighed mode methods, were utilized. Sensitivity analysis, including Cochrane's Q test, MR-Egger intercept test, and leave-one-out analysis, was applied to confirm the robustness of results. Among the 3117 bacterial taxa examined, we observed that 14 OM, like s_Streptococcus_mitis, s_Streptococcus_pneumoniae, and s_Haemophilus, were positively associated with epilepsy, while 7 OM, like g_Fusobacterium and g_Aggregatibacter, were negatively related to epilepsy. The MR-Egger intercept suggested that no evidence of horizontal pleiotropy was observed (p > 0.05). The leave-one-out analysis validated the robustness of the results. This study underscores the effect of OM on epilepsy, suggesting potential mechanisms between the OM and epilepsy. Further investigation into the potential role of the OM is needed to enhance our in-depth understanding of the pathogenesis of epilepsy. Previous research has demonstrated that the microbiota may influence the onset of epileptic seizures. We applied 3117 oral microbiota from the newest publicly available database of East Asian populations. Mendelian randomization analysis was utilized to estimate the causal relationship between oral microbiota and epilepsy. Our results showed that a causal effect exists between 21 oral microbiota and epilepsy. We provided genetic evidence for risk assessment and early intervention in epilepsy.

Comparative Study of Water Quality Prediction Methods Based on Different Artificial Neural Network

The prediction of future data using existing data is an effective tool for regional planning and watershed management. The back propagation neural network （BPNN） and convolutional neural network （CNN） were used to construct a prediction model based on the water quality index of Hengyang in Xiangjiang River Basin from April to May 2022 and the results of permanganate index prediction by different models were compared. The prediction results displayed by BPNN could predict the water quality； however, overfitting occurred during the prediction. BPNN modified by particle swarm optimization （PSO） could avoid overfitting, which improved the parameter selection method of the BPNN mode. The CNN model had a better prediction effect, which had a more complex structure and a more scientific fitting method to avoid the model falling into the local extreme value during the fitting process and improve the accuracy of the model prediction results. The evaluation parameters including root-mean-square error （RMSE）, coefficient of determination （R2）, and mean absolute error （MAE） were used to predict the accuracy of the network. Compared with that of the traditional BPNN model, PSO-BPNN reduced the RESM of the test set from 0.278 2 mg·L-1 to 0.210 9 mg·L-1, reduced the MAE of the test set from 0.222 3 mg·L-1 to 0.153 7 mg·L-1 and increased the R2 of the test set from 0.864 0 to 0.921 8, which indicated that PSO-BPNN had more stable fitting ability. RMSE, MAE, and R2 of the test set in the CNN model were 0.122 0 mg·L-1, 0.092 7 mg·L-1, and 0.970 5, respectively, which showed that CNN had a better fitting and prediction effect than that of BPNN.

Strain response prediction of offshore wind turbine tower under free vibration

Strain monitoring of critical locations in offshore wind turbine tower is essential for fatigue life prediction and safe operation of wind turbine. This paper theoretically analyses the feasibility of predicting tower strain under free vibration using the modal superposition method. A finite element numerical model of the tower is established, and the displacement mode and strain mode parameters of the tower are extracted. The initial displacement and strain of the tower at cut-out wind speed are analyzed, and the first three modal coordinate of the tower are calculated using test points of displacement or strain separately. Subsequently, the full field strain of the tower is predicted using the modal superposition method. Comparative results demonstrate higher accuracy in modal coordinate values calculated using test points of displacement, and the accuracy and errors of strain prediction using different numbers of test points are compared, emphasizing the crucial role of selecting optimal positions and numbers of test points in improving prediction accuracy.

Association of the gut microbiome and different phenotypes of COPD and asthma: a bidirectional Mendelian randomization study.

Mounting evidence has revealed the association between gut microbiota and both chronic obstructive pulmonary disease (COPD) and asthma; however, the causal association between gut microbiota and specific disease phenotypes remains to be determined. This study employed bidirectional two-sample Mendelian randomization (MR) analyses to investigate the potential causal relationship between gut microbiota and these conditions. The research utilized genome-wide association study (GWAS) data from the MiBioGen consortium for gut microbiota and the integrative epidemiology unit (IEU) Open GWAS for these conditions. Four MR analysis methods were employed: the inverse variance weighted (IVW) test, MR-Egger, weighted median, and weighted mode methods. The IVW method results are considered the primary findings. Sensitivity analyses, including heterogeneity tests, horizontal pleiotropy analysis, and leave-one-out analysis, were used to enhance robustness. Our MR study identified eight gut microbiota taxa potentially associated with the risk of different types of COPD and asthma. These include two taxa for early-onset COPD: Streptococcaceae [odds ratio (OR) = 1.315, 95% confidence interval (CI) = 1.071-1.616, P = 0.009] and Holdemanella (OR = 1.199, 95% CI = 1.063-1.352, P = 0.003); three for later-onset COPD: Acidaminococcaceae (OR = 1.312, 95% CI = 1.098-1.567, P = 0.003), Holdemania (OR = 1.165, 95% CI = 1.039-1.305, P = 0.009), and Marvinbryantia (OR = 0.814, 95% CI = 0.697-0.951, P = 0.009); one for allergic asthma: Butyricimonas (OR = 0.794, 95% CI = 0.693-0.908, P = 0.001); and two for non-allergic asthma: Clostridia (OR = 1.255, 95% CI = 1.043-1.511, P = 0.016) and Clostridiales (OR = 1.256, 95% CI = 1.048-1.506, P = 0.014).IMPORTANCEIndividuals with diverse phenotypes of chronic obstructive pulmonary disease (COPD) and asthma exhibit different responses to the conventional "one treatment fits all" approach. Recent research has revealed the significant role of the gut-lung axis in both COPD and asthma. However, the specific impact of gut microbiota on different subtypes of these conditions remains poorly understood. Our study has identified eight gut microbiota that may be associated with the risk of different types of COPD and asthma. These findings provide evidence suggesting a potential causal relationship between gut microbiota and various phenotypes of COPD and asthma. This offers a new perspective on the origins of different disease phenotypes and points toward future exploration of phenotype-specific and personalized therapies.

Synchronous LoRa sensor nodes for modal identification in footbridge vibration monitoring

AbstractThis article aims at presenting a preliminary investigation of using long-range and low-cost LoRa (Long Range) technology and two synchronous LoRa sensor nodes for cost-effective footbridge structural health monitoring (SHM). Two sensor nodes with LoRa modules and accelerometers were employed for vibration monitoring and a new attempt was made to use synchronous LoRa nodes for modal identification. In this article, a modal identification method based on a lightweight synchronization concept was proposed. This method is able to identify the fundamental mode of vibrating beam structures. Meanwhile, maximum accelerations can also be tracked periodically from the simultaneously recorded acceleration data by the synchronous LoRa nodes. Specifically, synchronization was achieved through the wireless peer-to-peer (P2P) communication between the two LoRa nodes, with the aim of initializing simultaneous acceleration recordings. The fundamental frequency and the phase information derived from the on-board calculation of the synchronized nodes can then be used to effectively identify the vertical bending mode and torsion mode. A series of laboratory tests were also conducted on a beam structure for the purpose of validation. The test results showed that the fundamental mode of the vibrating beam can be obtained rapidly and accurately using the synchronized LoRa nodes, with an average synchronization accuracy of 4.45 ms. The maximum acceleration data recorded by the LoRa nodes also showed high accuracy when compared with the raw acceleration data collected from a commercial Bluetooth accelerometer node. The fundamental frequencies obtained from both types of nodes also compare reasonably. The proposed modal identification method using two synchronous LoRa sensor nodes provides a basis for the development of a low-cost footbridge SHM system with the integration of IoT techniques. An attempt has been made to perform a preliminary field test on a cable-stayed footbridge, where the fundamental frequency and the mode shape type of the footbridge were successfully identified and its serviceability condition was also found to satisfy the code requirements.

Evaluation of physical risk factors by fuzzy failure mode and effects analysis: an apparel mill example

This study investigates the evaluation of risks faced by employees in a selected large-scale apparel mill using a risk assessment method with a fuzzy logic approach. The study found that risk assessment in the apparel industry is more accurate and reliable when using a fuzzy logic approach. Measurements were taken in three different time periods for noise, vibration, thermal comfort and lighting, which are physical risk factors. To evaluate the identified failure modes, the Failure Mode and Effects Analysis (FMEA) method was first applied. Then, for the same failure modes, the fuzzy FMEA method was applied using Fuzzy Logic Designer, an add-on to MATLAB. The study concludes that the fuzzy FMEA method can be a more useful method for risk assessment in the apparel industry.

A service failure assessment model for smart product consumption experience based on customer perception

Customer perception is an important consideration factor in evaluating the quality of human-computer interaction services. Sustainable user experiences and marketing strategies can be created by analyzing customer perception. By understanding consumer satisfaction with product services in the customer perception area, appropriate product service failure prevention strategies can be formulated. A service failure evaluation model is proposed in this study, which considers the customer tolerance area to accurately evaluate consumers’ behavioral experiences from purchasing to using products. The concept of tolerance area is introduced, and a combination of the fuzzy Failure Mode and Effect Analysis (FMEA) method and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is used to construct a human-computer interaction service failure evaluation model. Potential service failure factors of smart speakers are accurately evaluated by this model, and these service failure factors are ranked within the tolerance area. The research identifies voice misinterpretation and signal connectivity issues as the primary risk factors impacting the quality of human-computer interaction for smart speakers. The application of this method not only enhances the evaluation of smart speaker human-computer interaction services quality but also aids in the precise identification and prioritization of critical failure modes. The proposed service failure prevention strategies can reduce consumer dissatisfaction and provide innovative references for smart product design and marketing. The findings bolster empirical evidence for service failure prevention strategies in smart products and pave the way for novel perspectives on enhancing the quality of human-computer interaction services.

Causal relationship between lymphocyte subsets and the risk of sepsis: A Mendelian randomization study.

Recent empirical research posits a link between lymphocyte subgroups and both the incidence and prognosis of sepsis. Nevertheless, the potential influence of multiple confounding variables obscures any clear causative correlation. Utilizing a 2-sample Mendelian randomization approach, we conducted a meta-analysis of lymphocyte subgroups. In a genome-wide association study, flow cytometry was applied to a lymphocyte subgroup comprising 3757 Sardinians to identify genes influenced by blood immune cells. The sepsis meta-analysis data were sourced from the UK Biobank database, including 11,643 treatment groups and 47,841 control groups. Inverse variance-weighted, Mendelian randomization-Egger regression, weighted median, simple mode, and weighted mode methods were deployed to ascertain the causative relationship between lymphocyte subgroup and sepsis. Cochran Q test, the Mendelian randomization-Egger intercept test, and funnel plots were leveraged to assess the robustness of study findings. The inverse variance-weighted analysis disclosed that the absolute count of CD4 regulatory T cells (CD4 Treg AC) within the lymphocyte subgroup has a causative link to an elevated risk of sepsis, with an odds ratio of 1.08 and a 95% confidence interval of 1.02 to 1.15 (P = .011). Compared to individuals not subjected to this factor, those exposed to CD4 Treg AC have a marginally elevated sepsis risk by approximately 0.08%. No causative relationships were observed between sepsis risk and the absolute counts of other lymphocyte subgroups such as CD8+ T cells, CD4+ CD8dim T cells, natural killer T cells, B cells (B cell absolute count), and HLA DR+ natural killer cells. The 2-sample Mendelian randomization study indicated a causal relationship between the level of CD4 Treg AC and the increased risk of sepsis. The elevation in circulating lymphocyte subgroups suggests higher susceptibility to sepsis, affirming the immune susceptibility inherent to this condition. The findings from our study may propose potential targets for diagnosis and intervention of sepsis.

Bridge modal identification method based on adaptive VMD-HT algorithm

ABSTRACT Bridge engineering structures play a crucial role in transportation. Traditional methods of road and bridge closures for inspection and evaluation, while effective, increase bridge inspection costs and disrupt normal traffic flow. Therefore, a rapid detection and evaluation method for bridge structural modal parameters in the operational state is more suitable for practical engineering applications. However, under real operating conditions, bridge vibration signals typically contain a large amount of noise and interference, and modal identification must overcome issues of non-stationarity and non-linearity in the vibration signals caused by vehicle loads and environmental changes to ensure the accuracy of the identification. This study replaces the traditional Empirical Mode Decomposition algorithm in the Hilbert-Huang Transform with the Variational Mode Decomposition algorithm. It proposes a new method that combines the VMD algorithm with the Hilbert Transform, referred to as the Variational Mode Decomposition-Hilbert Transform method. Compared to the EMD algorithm, it offers significant improvements in suppressing mode mixing and endpoint effects. A vehicle-bridge coupling model was constructed, and the bridge’s natural modal information was successfully extracted from the vibration response signals. The research results indicate that the improved algorithm offers higher accuracy and faster computational speed compared to traditional identification algorithms.

Silicone Rubber-Packaged FBG Sensing Information and SSI-COV-Recognized Modal Parameters Motivated Damage Identification in Pipe Structures

Pipes are the main structures serving as the lifeline for oil and gas transportation. However, they are prone to cracks, holes and other damages due to harsh working environments, which can lead to leakage incidents and result in significant economic losses. Therefore, the development of structural health monitoring systems with advanced online diagnostic methods is of great importance for identifying local damages and assessing the safety state of pipe structures. These efforts can guide rapid repairs and ensure the continuous, efficient and cost-effective transportation of oil and gas resources. To address this problem, this paper proposes the development of a pipe monitoring system based on quasi-distributed fiber Bragg grating (FBG) sensing technology. The SSI-COV method is employed to process the sensor responses and extract the modal parameters of the structure. Based on this foundation, an enhanced damage identification index is proposed, which mitigates the effects of support and excitation positions on damage identification. The pipe structure can be regarded as a continuous super-statical beam, and based on its structural symmetry, a unit structure, specifically a stainless-steel pipe with fixed ends, is regarded as the experimental subject. Impact experiments have been conducted to analyze its behavior in both undamaged and damaged states. The research indicates that by using the proposed modal parameter identification method and the ASMDI damage index, ASMDI exhibits peak values at damage locations of the pipe structure. This allows for the identification of structural damage with high accuracy, fast processing efficiency and strong robustness. The study provides an effective and reliable damage diagnosis method, which can contribute to the refinement and visualization of pipe structural health monitoring systems.

Artificial Sweetener and the Risk of Adverse Pregnancy Outcomes: A Mendelian Randomization Study.

The relationship between the intake of artificial sweetener (AS) and adverse pregnancy outcomes is under-researched, and existing studies yield inconsistent conclusions. A Mendelian randomization (MR) approach was employed to investigate the causal relationship between the intake of AS and adverse pregnancy outcomes. Instrumental variables related to the exposure phenotype were selected for analysis. The analysis was conducted using genome-wide association study summary data from public datasets. The inverse variance weighted, MR-Egger, weighted median, simple mode, and weighted mode methods were used to evaluate the causal relationship between exposure and outcomes. Sensitivity analysis and multivariable Mendelian randomization enrolling body mass index, type 2 diabetes mellitus, and fasting glucose were employed to further validate the consistency and robustness of the results. In univariable MR, the intake of AS added to tea was associated with an increased risk of ectopic pregnancy [OR = 1.821 (1.118-2.967), p = 0.016]. In multivariable MR adjusting for body mass index and type 2 diabetes mellitus, the intake of AS added to cereal was linked to a reduced risk of ectopic pregnancy [OR = 0.361 (0.145-0.895), p = 0.028] and premature rupture of membranes [OR = 0.116 (0.019-0.704), p = 0.019], while the intake of artificial sweetener added to coffee was associated with an increased risk of placenta previa [OR = 1.617 (1.042-2.510), p = 0.032]. No causal relationship was identified between the intake of artificial sweetener and other adverse pregnancy outcomes. The consumption of artificial sweetener during pregnancy warrants careful consideration.

Piezoelectric energy harvesting from walking motion of a passive biped robot model with flexible legs

Passive dynamic walking biped robots utilize the inherent dynamics of their structure to achieve walking motion without continuous actuation, enhancing energy efficiency and stability. Replacing conventional rigid legs with flexible elastic beams in the simplest passive walker will be associated with undesirable vibrations. Despite the positive effect of the damper in reducing vibrations, the current research has used the piezoelectric energy harvesting with the aim of reducing the wasted energy of the gait cycle and improving its stability range. For this purpose, the electromechanical Lagrange equations are determined by applying Hamilton's principle and the assumed mode method and then, the new definition of the step function is obtained through numerical methods, solving a boundary value problem to establish proper initial conditions for stable walking. Numerical simulations indicate that the piezoelectric energy harvester can create a stable period-one gait cycle by optimizing the length of the piezo layers attached to the undamped elastic legs despite the presence of small vibrations. Additional results are presented in bifurcation diagrams, investigating the effect of important physical and structural parameters such as slope angle, length and thickness of the piezo layer.

Assessing the causal relationship between gut microbiota and prostate cancer: A two-sample Mendelian randomization study

BackgroundNumerous studies indicate that the gut microbiome is closely associated with prostate cancer (PCa), however, owing to various confounding factors, the causal relationship between gut microbiota and PCa remains unclear. MethodsA 2-sample Mendelian randomization (MR) analysis utilized genome-wide association study (GWAS) data on the gut microbiota of 18,340 participants and GWAS summary statistics on PCa involving 46,3010 participants. Inverse variance weighted (IVW) served as the primary method, complemented by the MR-Egger method, weighted median method (WME), simple mode method (SM), and weighted mode method (WM). Finally, to confirm the robustness of the results, heterogeneity test, pleiotropy test, and leave-one-out sensitivity test were conducted. ResultsIVW analysis revealed that 12 specific gut microbial taxa were potentially causally associated with PCa; the genera Victivallis, Akkermansia, Odoribacter, Butyrivibrio, and the families Enterobacteriaceae, Verrucomicrobiaceae, as well as the orders Verrucomicrobiales, Enterobacteriales and the class Verrucomicrobiae, were found to be positively associated with PCa risk. Conversely, the genera Eubacterium ruminantium group, Candidatus Soleaferrea, and RuminococcaceaeUCG003 were negatively associated with PCa risk. ConclusionsOur MR study's results support a genetically predicted causal relationship between the gut microbiota and PCa, and we identified 12 specific gut microbial taxa. These findings could offer new targets for PCa screening and treatment.

Physics-informed neural networks in support of modal wavenumber estimation.

A physics-informed neural network (PINN) enables the estimation of horizontal modal wavenumbers using ocean pressure data measured at multiple ranges. Mode representations for the ocean acoustic pressure field are derived from the Hankel transform relationship between the depth-dependent Green's function in the horizontal wavenumber domain and the field in the range domain. We obtain wavenumbers by transforming the range samples to the wavenumber domain, and maintaining range coherence of the data is crucial for accurate wavenumber estimation. In the ocean environment, the sensitivity of phase variations in range often leads to degradation in range coherence. To address this, we propose using OceanPINN [Yoon, Park, Gerstoft, and Seong, J. Acoust. Soc. Am. 155(3), 2037-2049 (2024)] to manage spatially non-coherent data. OceanPINN is trained using the magnitude of the data and predicts phase-refined data. Modal wavenumber estimation methods are then applied to this refined data, where the enhanced range coherence results in improved accuracy. Additionally, sparse Bayesian learning, with its high-resolution capability, further improves the modal wavenumber estimation. The effectiveness of the proposed approach is validated through its application to both simulated and SWellEx-96 experimental data.

Free vibration analysis of multibladed propeller propulsion system based on substructure modal synthesis method

Free vibration analysis of multibladed propeller propulsion system based on substructure modal synthesis method