Variable Model Research Articles

Risk assessment of goaf instability based on a variable weight model

Risk assessment of goaf instability based on a variable weight model

Study on the Weibull distribution function-based stochastic damage evolution law for uniaxial compression in high-performance concrete with full aeolian sand

The issues of environmental degradation due to natural sand overextraction are becoming increasingly severe. In desert regions, utilizing aeolian sand as a substitute for natural sand in construction projects facilitates the resource economization and the effective management of desertification. The concrete industry is expected to significantly contribute to this objective, as high-performance concrete with full aeolian sand (FA-HPC) has demonstrated excellent mechanical properties. However, current researches have yet to establish a rational damage evolution law for uniaxial compression in FA-HPC from the perspective of damage characteristics. In this study, uniaxial compression tests were conducted on FA-HPC with different mixture proportions. Simultaneously, the stress-strain behavior was examined, and acoustic emission (AE) signals were collected and localized. Based on the Weibull distribution function-based stochastic damage evolution law for uniaxial compression, regression analyses of the stress-strain and AE cumulative ringing count for FA-HPC were performed. Damage evolution model for effective stress and damage variable were established and validated with experimental data to confirm their applicability. The scale and shape parameters of the model were predicted using the mixture proportions as independent variables. The results show that the rationality of the damage evolution law assumption was validated through AE source localization. The goodness of fit values for damage variable and effective stress equation range from 0.87 to 0.98, with the ascending segment and the vicinity of the peak point closely matching the experimental data, indicating that the Weibull distribution function-based stochastic damage model for uniaxial compression is applicable for FA-HPC. The predicted values of the damage evolution model parameters based on the mixture proportions align well with the measured values, achieving a goodness of fit of 0.99, thereby guiding the study of FA-HPC's compressive characteristics prior to the formation of primary cracks. Notably, there remains a need to propose corrective equations to enhance the correlation between regression curves and experimental data in the late failure stages of FA-HPC in future research, achieving precise predictions by considering the interactions of multiple factors.

Latent Vector Autoregressive Modeling: A Stepwise Estimation Approach

Researchers often study dynamic processes of latent variables in everyday life, such as the interplay of positive and negative affect over time. An intuitive approach is to first estimate the measurement model of the latent variables, then compute factor scores, and finally use these factor scores as observed scores in vector autoregressive modeling. However, this approach neglects the uncertainty in the factor scores, leading to biased parameter estimates and threatening the validity of conclusions about the dynamic process. We propose Three-Step Latent Vector Autoregression that adheres to this stepwise procedure while correcting for the factor scores’ uncertainty. Stepwise approaches offer various advantages, for example the ability to visualize and inspect the factor scores. A simulation study demonstrates that the method performs well in obtaining correct parameter estimates of a dynamic process. We also provide an empirical example and scripts for implementation in the open-source software R using the lavaan package.

Soft sensor modeling based on self-organizing fuzzy neural network with clustering, merging, and splitting scheme

Abstract As a pivotal role in the control, optimization, and monitoring of contemporary industrial processes, soft sensors are frequently employed in the prediction of key quality variables. To achieve accurate prediction of key quality variables in industrial processes, a soft sensor modeling method based on the self-organizing fuzzy neural network with the clustering, merging, and splitting scheme (SOFNN-CMS) is proposed. First, the supervised fuzzy C-means clustering algorithm is proposed to identify the appropriate initial center and width of the fuzzy neural network, obtaining appropriate initial fuzzy rules. Then, a neuron merging and splitting strategy is designed to adjust the structure of the fuzzy neural network, by merging and splitting the hidden neurons according to the distance of clusters, increasing the adaptability of the fuzzy neural network. Besides, to accelerate the convergence of estimation errors, an improved Levenberg Marquardt algorithm is utilized to update neural network parameters in the training phase, realizing the soft sensor modeling of key quality variables. The effectiveness of the proposed SOFNN-CMS neural network is demonstrated on two benchmark problems and an industrial debutanizer column. Finally, the experiments showcase that the proposed SOFNN-CMS neural network can obtain better soft sensor modeling performance with a compact structure.

Application of Flores model considering variable recovery coefficient in dynamics analysis of ball bearings

PurposeThis paper aims to select an appropriate contact force model and apply it to the interaction model between the balls and the cage in the rolling bearings to describe the elastic–plastic collision phenomena between the two.Design/methodology/approachTaking the ball–disk collision mode as an example, several main contact force models were compared and analyzed through simulation and experiment. In addition, based on the consideration of yield strength of materials and initial collision velocity, a variable recovery coefficient model was proposed, and its validity and accuracy were verified by the ball–disk collision experiments. Then, respectively, the Flores model and the Hertz model were applied to the interaction between the balls and the cage, and the dynamics simulation results were compared.FindingsThe results indicate that the Flores model has good regression of recovery coefficient, indicating good applicability for both elastic and elastic–plastic contacts and can be applied to the contact collision situations of various materials. Under certain working conditions, there are significant differences in the dynamics results of rolling bearings simulated using the Flores model and Hertz model, respectively.Originality/valueThis paper applies the Flores model with variable recovery coefficients to the dynamics simulation analysis of ball bearings to solve the elastic–plastic collision problem between the rolling elements and the cage that cannot be reasonably handled by the Hertz model.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0138/

Association between Internet use and depression among older adults in China: the chain-mediating role of volunteer activity participation and friend network.

Depression is a significant burden on individuals and society, which requires our attention and action. As the aging wave collides with digitalization, further research is needed to understand how internet use relates to depression in older adults. This study aimed to investigate whether volunteer activity participation and friend networks played a chain mediating role in this relationship from the perspective of the socioemotional selectivity theory. This study's data was derived from the 2018 China Longitudinal Aging Social Survey (CLASS), comprising 5,558 samples. The study employed the OLS model for benchmark regression and multiple robust check methods, including altering variable settings and utilizing the instrumental variable model. In addition, the generalized structural equation model (GSEM) was used to analyze the mechanism. Using the internet was significantly associated with reduced depression in older adults (coefficient = -0.9321, p < 0.001). The instrumental variable model confirmed this reduction (coefficient = -0.9200, p < 0.001). Moreover, we found that there were three indirect pathways of Internet use on depression among older adults: the mediating role of volunteer activity participation (all p-values <0.001), the mediating role of friend network (all p-values <0.001), and the chain mediating role of both factors (all p-values <0.001). Based on the research findings, we suggest mobilizing tech-savvy older adults to assist others in adopting digital technology and using the internet. We also suggest that the government could assist in creating older adult-friendly communities. Social workers could collaborate with tech-savvy older adults to organize various voluntary activities, encouraging more senior citizens to participate. In addition, we recommend that the community should consider the form of activities to help older adults make friends first rather than solely focusing on voluntary activities.

Monitoring Anesthetic Depth Using the Patient State Index in Electroconvulsive Therapy Improves Seizure Quality.

The determination of anesthetic depth has been used to assess the optimal moment for applying electrical stimuli in electroconvulsive therapy (ECT), as some of the anesthetics used can reduce its effectiveness. In this study, seizure quality was assessed using anesthetic depth measurement with the patient state index (PSI). A prospective experimental study was conducted with a control group, including a sample of 346 stimulations (PSI=134; Control=212) in 51 patients admitted and diagnosed with major depressive disorders. Seizure adequacy variables (seizure time in electroencephalogram [EEG] and motor activity, visual evaluation of the EEG, ECT-EEG parameter rating scale [EEPRS], seizure concordance, central inhibition, automated parameters, and autonomic activation) were assessed using linear mixed-effects models for continuous variables and generalized linear mixed-effects models for dichotomous variables. The PSI group required lower stimulation energy. The use of the PSI was associated with longer seizure time, both motor and electroencephalographic, higher quality of the EEG recording, better seizure concordance, and higher values for the automated parameters of maximum sustained coherence and time to peak coherence. The use of the PSI to measure anesthetic depth may reduce the electrical stimulus charge required and improve seizure quality in ECT modified with propofol.

Factors Influencing the Participation in Defined Contribution Pension Scheme by Urban women in the informal Sector in Ghana

The study set out to investigate reasons for the low interest in participating in Defined Contribution Pension Schemes (DCPS) by urban women in the informal sector in Ghana (UWISG) in order to unravel the causes of the phenomenon of non-participation so that solutions can be identified. Most studies conducted on voluntary participation in pension schemes have not explored the such variables; perceived net pension wealth, permanent income, attitude, subjective norm, and perceived behavioural control. The main research questions among others are; what are the effects of demographic characteristics of urban women in the informal sector in Ghana (UWISG) on their intention to participate in Defined Contribution Pension Schemes (DCPS)? What is the impact of perceived net pension wealth (perceived benefits) on the intention of UWISG to participate in DCPS? The Methodology of the study is quantitative research design. The study has two research models a logistic regression model of demographic variables and model is a framework consisting of exogenous and endogenous latent variables. Some significant findings of the study are (i) an inverse relationship between disposition to save and intention to participate in DCPS. The inverse relationship implied that most of the respondents would prefer to probably put money in their savings or investment accounts rather than contributing to DCPS. The study also found the age variable to be an insignificant influencer of DCPS adoption intention.

Cardiovascular Disease, Brain Glucose Metabolism, and Neurocognitive Decline in People With Human Immunodeficiency Virus.

Cardiovascular disease (CVD) and neuroinflammation are thought to exacerbate neurocognitive dysfunction in treated people with human immunodeficiency virus (PWH). Here, we longitudinally measured brain glucose metabolism as a measure of neuronal integrity in treated PWH using [18F]Fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) in correlation with atherosclerotic cardiovascular disease (ASCVD) scores, cerebrospinal fluid (CSF) neuroinflammatory markers, neurocognitive outcomes, and other clinical and laboratory variables (CLVs). Well-controlled PWH (n = 36) underwent baseline and follow-up FDG PET/CT obtained 3.5 years apart on average. Longitudinal changes in whole brain and regional relative FDG uptake, brain volumes, CLVs, CSF cytokines, and neuropsychological measures were measured. A variable selection model identified baseline variables related to future brain metabolic changes while multivariable models explored neuropsychological implications of brain metabolism and volumetrics. High ASCVD scores predicted future decreased thalamic uptake (slope = -0.0068, P = .027) and decreasing thalamic uptake correlated with worsening cognition (slope = 15.80, P = .020). Despite longitudinal greater than expected gray matter loss, whole brain FDG uptake did not change over the follow-up period. Most CSF cytokines decreased longitudinally but were not predictive of FDG changes. We found that high ASCVD scores in a group of treated PWH were related to thalamic hypometabolism, which in turn correlated with neurocognitive decline. Our findings support the contribution of CVD to neurocognitive dysfunction. More proactive CVD management may have a role in mitigating progression of cognitive impairment. Lack of change in global brain glucose metabolism despite documented accelerated gray matter volume loss over the same period suggests that FDG PET might underestimate neuronal injury in PWH compared to structural magnetic resonance imaging.

Ultrasound biomicroscopy in dogs suggests postoperative ocular hypertension may be associated with ciliary cleft changes related to cataract surgery.

To determine whether novel pre- and postsurgical ultrasound biomicroscopy (UBM) measurements of the canine ciliary cleft (CC) are associated with postoperative ocular hypertension (POH) following cataract surgery and to explore the relationship between intraocular pressure and CC UBM measurements. Following pharmacologic mydriasis, UBM images were obtained from 31 client-owned dogs before elective cataract surgery, immediately postsurgery, and 4 to 6 hours following surgery or while experiencing POH ≥ 25 mm Hg. Presurgery and the pre- to postsurgery change in CC measurements were assessed for association with POH using individual mixed-effects logistic regression models and forward variable selection models. Linear mixed-effects models were used to evaluate the relationship of intraocular pressure to UBM measurements within the same eye across multiple time points. Presurgical measurements were not predictive of POH development. An increase in pectinate ligament distance and CC area from presurgical baseline to immediate postsurgical measurement was associated with reduced odds of developing POH, while increasing CC length (from apex to mid point on the pectinate ligament) from pre- to postsurgery and immature cataracts was associated with increased odds of POH. The change in CC morphology following cataract surgery appears more impactful in the development of POH than individual variations in presurgery CC measurements. Several changes in the CC dimensions following surgery appear associated with POH risk. This understanding of a potential mechanism of POH development opens new avenues for researching preventative measures associated with modifying surgical techniques to influence CC morphology following cataract surgery.

A survey of the psychiatric care provided for children and young people in general hospital settings in New Zealand

Objective The aim of this study was to conduct an in-depth survey of psychiatric care provided for children and young people (CYP) in general hospital settings in New Zealand (NZ). Method As part of a larger national survey of Consultation-Liaison Psychiatry (CLP) services across the lifespan, a 44-question survey was emailed to clinicians who were involved in providing psychiatric care for CYP at each of the 24 public general hospitals with specialist paediatric services. Results Responses were obtained from all four paediatric CLP teams that cover the four specialist children’s hospitals, and 16 of the 23 child and adolescent community mental health services that provide hospital inreach. These services were found to be under-resourced, utilise variable service models, and rely heavily on inreach. Conclusion Escalating presentation rates for young people and increasingly complex paediatric presentations pose major challenges for the psychiatric care of CYP in general hospital settings. Utilising international staffing standards and service models, proposals are made to evolve more consistent and effective paediatric CLP services in NZ.

Analysis of fencers’ post-injury adjustment: confirmatory factor analysis

Background and purpose Self-Adjustment is an important factor that every post-injury fencer must consider. Purpose. The aim of this research is to assess the construct validity and reliability of the self-adaptation scale, test the contribution of each aspect and indicator in reflecting the self-adaptation variable, and evaluate the suitability of the theoretical model self-adaptation with empirical data. Self-adaptation is measured through seven aspects, wich include self-regulation, frustration reduction, rational thinking, practical skills, past experiences, reality and goals, and defense mechanisms. Materials and methods The sample in this study was 227 respondents who were fencing athletes who had recovered from injuries. Respondents consisted of 132 men (58.1%) and 95 women (41.9%). The sampling technique employed was purposive sampling, based on specific criteria. To gather the data, a self-adaptation scale was utilized, and the data was subjected to analysis using Structural Equation Modeling (SEM) with second-order CFA via the Lisrel 8.71 software. Results The study’s analysis revealed that injured fencer athletes in Indonesia demonstrate a high level of self-adaptation. The aspects and indicators forming the self-adaptation variable were found to be valid and reliable based on the analysis results. In this context, the training aspect stands out as the most influential factor in reflecting self-adaptation, particularly the ability to practice within the training environment. However, emotional control emerges as the lowest aspect in reflecting self-adaptation, particularly concerning risk perception in their actions. The measurement model of the self-adaptation variable aligns well with the empirical data. To assess the model fit, primary criteria such as Chi-square, RMSEA, RMR, GFI, NFI, NNFI, CFI, IFI, and RFI were employed. Conclusions This research aims to assess the congruence between the theoretical model of self-adaptation variables and the empirical data obtained in the field.

Association between dietary mineral intakes and urine flow rate: data from the 2009-2018 National Health and Nutrition Examination Survey.

Minerals play an important role in human health, but their effect on urinary function remains controversial. The aim of this study was to assess the association between dietary intake of minerals (Ca, P, Mg, Fe, Zn, Cu, Na, K, Se) and urine flow rate (UFR). We conducted a cross-sectional study using the National Health and Nutrition Examination Survey (NHANES, 2009-2018) database. Multivariate regression and smooth curve fitting were used to investigate the association between dietary mineral intakes and UFR. Subgroup analyses and interaction tests were used to investigate whether this association was stable in the population. Our study involving 10,229 representative adult NHANES participants showed an association between Mg intake and UFR in a linear regression model for continuous variables. And in the model analysis of tertile categorical variables, we observed a positive association between six mineral intakes (Ca, Mg, Zn, Cu, Na, and K) and UFR. Smoothed curve fitting and threshold effect analysis further support the nonlinear relationship between mineral intakes and UFR. Subgroup analyses and interaction tests ensured the reliability and robustness of the findings. This study examined the effects of nine dietary minerals on UFR and found that intake of Ca, Mg, Zn, Cu, Na, and K were positively correlated with UFR, suggesting that these minerals may have a positive effect on improving urinary function. In particular, Mg showed a more significant positive correlation with UFR in women, while Na showed a stronger positive correlation in diabetics. However, P, Fe and Se did not show significant correlations. In summary, although these findings provide a preliminary understanding of the relationship between dietary minerals and urinary function, further prospective studies are still necessary to validate these relationships and explore the physiologic mechanisms underlying them.

Machine learning and multiple linear regression models can predict ascorbic acid and polyphenol contents, and antioxidant activity in strawberries.

Strawberry is a rich source of antioxidants, including ascorbic acid (ASA) and polyphenols, which have numerous health benefits. Antioxidant content and activity are often determined manually using laboratory equipment, which is destructive and time-consuming. This study constructs a prediction model for antioxidant compounds utilizing machine learning (ML) and multiple linear regression based on environmental, plant growth and agronomic fruit quality-related parameters as well as antioxidant levels. These were studied in three farms at two-week intervals during two years of cultivation. During the ML model screening, artificial neural network (ANN)-boosted models displayed a moderate coefficient of determination (R2) at 0.68-0.78 and relative root mean square error (RRMSE) at 3.8-4.8% in polyphenols and total ASA levels, as well as a high R2 of 0.96 and low RRMSE at <3.0% in antioxidant activity. Additionally, we developed variable selection models regarding the antioxidant activity, and variables two and five (environmental parameters and leaf length, respectively) with high accuracy were selected. The linear regression analysis between the actual and predicted data of antioxidants in the ANN-boosted models revealed high fitness with all parameters in almost all training, validation and test sets. Furthermore, environmental parameters are essential in developing such reliable models. We conclude that ANN-boosted, stepwise and double-Lasso regression models can predict antioxidant compounds with enhanced accuracy, and the relevant parameters can be easily acquired on-site without the need for any specific equipment. © 2024 Society of Chemical Industry.

Classified dataset, regression and machine learning modeling for prediction of phase transformation temperatures in steels

The prediction of the characteristic Martensite Start (Ms) temperature and Austenitic Nose Tip Temperature (ANTT) in steels is of scientific and technological importance; however, it faces significant challenges due to multiphysical complexity.In this study, we introduced a structured framework for data classification and hierarchical iterations aimed at predicting Ms (Martensite start temperature) and ANTT (Austenite non-transforming temperature). This framework was incorporated into two optimization models, leading to enhancements in accuracy, extrapolation capabilities, and generalization performance. First, we classified the collected Ms datasets hierarchically based on the alloying elements presented in steels, including carbon, austenite stabilizers, non-austenitization elements, and data credibility. Regression analyses of Ms temperatures concerning chemical compositions were then carried out using phenomenological variables from binary systems to multi-component systems in alignment with the spirit of CALPHAD modeling, which is renowned for its robust extrapolation abilities. By iteratively fitting the hierarchically classified datasets and implementing hierarchical iterations, we developed the CALPHAD-guided phenomenological variable (CGPV) Ms regression model. This model achieved improved accuracy levels, with R2 values of 0.9 for training and 0.87 for testing, surpassing most conventional regression models that do not account for compositional interactions. Furthermore, the CALPHAD-guided machine learning (CGML) model, constructed based on the classified datasets and hierarchical iterations but without utilizing phenomenological variables, demonstrated strong performance with R2 values of 0.98 and 0.86 for training and testing, respectively. The CGML model was demonstrated not only to reliably filter out problematic data in a dataset but also to unveil the unnoticed coupling between carbon and other alloying elements on Ms. Finally, the CGML method has been readily transferred to predict ANTT with high accuracy as well.

A Heteroscedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates

This article proposes an overidentifying restriction test for high-dimensional linear instrumental variable models. The novelty of the proposed test is that it allows the number of covariates and instruments to be larger than the sample size. The test is scale-invariant and robust to heteroscedastic errors. To construct the final test statistic, we first introduce a test based on the maximum norm of multiple parameters that could be high-dimensional. The theoretical power based on the maximum norm is higher than that in the modified Cragg-Donald test, the only existing test allowing for large-dimensional covariates. Second, following the principle of power enhancement, we introduce the power-enhanced test, with an asymptotically zero component used to enhance the power to detect some extreme alternatives with many locally invalid instruments. Finally, an empirical example of the trade and economic growth nexus demonstrates the usefulness of the proposed test.

A Bayesian complex-valued latent variable model applied to functional magnetic resonance imaging

Abstract In linear regression, the coefficients are simple to estimate using the least squares method with a known design matrix for the observed measurements. However, real-world applications may encounter complications such as an unknown design matrix and complex-valued parameters. The design matrix can be estimated from prior information but can potentially cause an inverse problem when multiplying by the transpose as it is generally ill-conditioned. This can be combat by adding regularizers to the model but does not always mitigate the issues. Here, we propose our Bayesian approach to a complex-valued latent variable linear model with an application to functional magnetic resonance imaging (fMRI) image reconstruction. The complex-valued linear model and our Bayesian model are evaluated through extensive simulations and applied to experimental fMRI data.

Stochastic Block Smooth Graphon Model

In this article, we propose combining the stochastic blockmodel and the smooth graphon model, two of the most prominent modeling approaches in statistical network analysis. Stochastic blockmodels are generally used for clustering networks into groups of actors with homogeneous connectivity behavior. Smooth graphon models, on the other hand, assume that all nodes can be arranged on a one-dimensional scale such that closeness implies a similar behavior in connectivity. Both frameworks belong to the class of node-specific latent variable models, entailing a natural relationship. While these two modeling concepts have developed independently, this article proposes their generalization toward stochastic block smooth graphon models. This combined approach enables to exploit the advantages of both worlds. Employing concepts of the EM-type algorithm allows to develop an appropriate estimation routine, where MCMC techniques are used to accomplish the E-step. Simulations and real-world applications support the practicability of our novel method and demonstrate its advantages. The article is accompanied by supplementary material covering details about computation and implementation.

Digital documentation of treatment data and their relation to breed and performance parameters of sows

Abstract. The emerging risk of resistance to antimicrobials has brought about the need to document animal production in the European Union. The required documentation of treatment data on-farm may withhold viable information, which can be analysed if made accessible in a digital format. The aim of the study was to use a digital system for the documentation of treatments and to investigate relationships between treatments, performance parameters and breeds. Data were collected between August 2020 and September 2022 on a combined pig farm in northern Germany. A digital tool was used to obtain data on treatments, while sows' performance information and breed data were drawn from the sow planner of the farm. Treatment indices were generated for the sows (TIS, treatment index sow) and the sows' litters (treatment index litter, TIL). During the time of data collection, 17 % (n = 113) of the farrowing events (n = 661) took place with farrowing-related treatments (FRTs), and 43.5 % (n = 94) of the sows had to receive FRT at least once. Linear mixed models were used to investigate the dependent variables TIS, TIL and FRT and the performance parameters piglets born alive, stillborn piglets and weaned piglets. The explanatory variables parity, season, year, the interaction of season and year, and the breed of the sow (crossbred Large White × Landrace and purebred Large White), as well as the random effect of the sow nested in the breed of the sow, were included in the models for all dependent variables. Additionally, the breed of the boar (Duroc, Pietrain), TIS, TIL and FRT were investigated in the models of the performance parameters. The following effects were all significant (p&lt;0.05). Parity had an effect on TIS, piglets born alive and stillborn piglets. Sows with parity of 1 had the highest least-squares mean (LSM) = 0.42 (standard error (SE) = 0.04) for TIS, which decreased over parities of 2–3, 4–5 and 6–7 (LSM = 0.16, SE = 0.04) and increased for parity of ≥8 (LSM = 0.34, SE = 0.05). While sows with parity of 1 produced the lowest numbers and sows with parities of 4–5 the highest numbers of piglets born alive, the number of stillborn piglets increased with parity number, with the lowest numbers for parity of 1 and the highest numbers for parity of ≥8. Overall, the performance of the farm improved in the second year of data collection with less FRT, more weaned piglets and fewer stillborn piglets. Crossbred sows produced more piglets born alive and weaned piglets but also more stillborn piglets. The use of Pietrain boars led to more piglets being born alive. FRT and high treatment indices of the sow related with an increase in stillborn piglets and high treatment indices of the sow resulted in fewer piglets born alive. In conclusion, digitalized treatment data allow for analyses of the herd's health status, and relations between production performance and treatments can be investigated.

Does Silodosin Improve Primary Ureteroscopy Access and Outcomes: Meta-Analysis of Randomized Controlled Trials.

Background: Ureteroscopy (URS) is a widely utilized procedure for the management of urinary stones, though failed access due to ureteral orifice tightness or spasms can be a potential outcome. Silodosin, an alpha-1A adrenergic receptor antagonist, has shown promise in recent randomized controlled trials (RCTs) in improving URS outcomes by relaxing ureteral smooth muscle. Objective: This systematic review and meta-analysis aims to determine whether preoperative administration of silodosin enhances ureteroscopy outcomes, including ureteral access rates, operative time, complication rates, and stone-free rates. Methods: After PROSPERO registration, a comprehensive search of PubMed, EMBASE, and the Cochrane Library was conducted for RCTs comparing silodosin with placebo or no medication before URS. Data extraction and bias assessment were performed independently by two reviewers. Statistical analysis was undertaken by Review Manager V5.4, employing random-effects models for heterogeneous variables. Results: Eight RCTs with a total of 892 patients (416 in the silodosin group and 476 in the control group) met the inclusion criteria. Silodosin significantly reduced operative time by 15.74 minutes (p < 0.00001). The access rate was higher in the silodosin group (96.9%) compared with the control group (87.2%)(p = 0.0004). Total complication rates were lower in the silodosin group (14.39% vs 27.47%, p < 0.00001), as were moderate to significant complications (5.0% vs 11.7%, p = 0.003). Stone-free rates were also higher in the silodosin group (92.16% vs 81.5%, p < 0.0001). Conclusion: Preoperative administration of silodosin significantly improves URS outcomes by reducing operative time, increasing access rates, decreasing complication rates, and enhancing stone-free rates. These findings support the integration of silodosin into clinical practice guidelines for URS, potentially improving procedural efficiency and patient outcomes.