Two-stage Approach Research Articles

A novel model-based Cauchy-Schwarz divergence condition indicator for gears monitoring during fluctuating speed conditions

Gear monitoring and fault diagnosis are vital for preventing accidents and minimizing economic losses in transportation and industrial systems. Traditional methods use vibration sensors and a two-stage analysis approach: preprocessing data to remove noise and extract relevant components, and generating a condition indicator to detect behavioral anomalies in the gears over time.Time synchronous averaging is a notable tool for monitoring gears at constant speeds. Such a tool filters sensor signals and extracts rotation-related components by using statistical measurements as condition indicators. However, it has limitations in scenarios with time-varying sampling rates and fluctuating speeds, where statistical measures may not fully capture changes in system parameters.This article proposes a novel methodology for monitoring gears in multivariate rotordynamical systems under fluctuating speed conditions. The method integrates time synchronous averaging, system identification algorithms, and statistical tools. It generates a time-synchronous average signal considering speed fluctuations, computes a state–space model of gear behavior in healthy states, extracts residual data from a data-driven model, and generates a condition indicator based on the Cauchy–Schwarz divergence.The proposed methodology was evaluated using experimental data from three rotor dynamical setups under different operational conditions. Validation showed its effectiveness, especially under high-load conditions with significant speed fluctuations.

Optimization of a Novel Engineered Ecosystem Integrating Carbon, Nitrogen, Phosphorus, and Sulfur Biotransformation for Saline Wastewater Treatment Using an Interpretable Machine Learning Approach.

The denitrifying sulfur (S) conversion-associated enhanced biological phosphorus removal (DS-EBPR) process for treating saline wastewater is characterized by its unique microbial ecology that integrates carbon (C), nitrogen (N), phosphorus (P), and S biotransformation. However, operational instability arises due to the numerous parameters and intricates bacterial interactions. This study introduces a two-stage interpretable machine learning approach to predict S conversion-driven P removal efficiency and optimize DS-EBPR process. Stage one utilized the XGBoost regression model, achieving an R2 value of 0.948 for predicting sulfate reduction (SR) intensity from anaerobic parameters with feature engineering. Stage two involved the CatBoost classification and regression model integrating anoxic parameters with the predicted SR values for predicting P removal, reaching an accuracy of 94% and an R2 value of 0.93, respectively. This study identified key environmental factors, including SR intensity (20-45 mg S/L), influent P concentration (<9.0 mg P/L), mixed liquor volatile suspended solids (MLVSS)/mixed liquor suspended solids (MLSS) ratio (0.55-0.72), influent C/S ratio (0.5-1.0), anoxic reaction time (5-6 h), and MLSS concentration (>6.50 g/L). A user-friendly graphic interface was developed to facilitate easier optimization and control. This approach streamlines the determination of optimal conditions for enhancing P removal in the DS-EBPR process.

Human–machine collaborative optimization method for dynamic worker allocation in aircraft final assembly lines

The allocation of workers with diverse skills on assembly stations significantly impacts the efficiency of aircraft final assembly (AFA). In a real-life assembly process, workers are allowed to move between multiple stations, to avoid bottleneck stations. The arrangement of worker movement is a practical issue coupled with the worker allocation, but is still ignored. This study formulates a dynamic worker allocation problem in AFA (dWA-AFA), focusing on the multiobjective joint optimization of worker allocation and worker movement. The trade-off between objectives depends on the human preferences that reflect assembly requirements. To address this, a human–machine collaborative optimization method (HMC-O) is proposed, involving a bidirectional collaboration: machine-to-human preference adaption, and human-to-machine experience support. Specifically, we design an adaptive dominance operator for integrating human preferences, and combine it with a two-stage nondominated sorting approach to generate initial optimization solutions. An experience-driven neighborhood search is further developed, which uses human experience to improve solutions. The proposed method is evaluated through two scales of real cases. It is observed the dWA-AFA significantly reduces the takt time, particularly by 14.21 % and 9.25 % in two worker-shortage scenarios respectively. Meanwhile, the HMC-O is competitive in terms of convergence and preference satisfaction for solving dWA-AFA.

Effectiveness of public health spending: Investigating the moderating role of governance using partial least squares structural equation modelling (PLS-SEM)

BackgroundThe link between public health spending (PHS) and population health outcomes (PHO) has been extensively studied. However, in sub-Saharan Africa (SSA), the moderating effects of governance in this relationship are little known. Furthermore, studies have focused on mortality as the main health outcome. This study contributes to this literature by investigating the moderating role of governance in the relationship by simultaneously assessing three dimensions of governance (corruption control, government effectiveness and voice accountability) using disability-adjusted life years (DALYs) as a measure of outcomes.MethodsThe study applies the two-stage moderation approach using partial least squares structural equation modelling (PLS-SEM) to panel data from 43 SSA nations from 2013 to 2019. The study also uses domestic general government health expenditure (DGGHE) as an independent variable and disability-adjusted life years (DALY) as the dependent variable in this relationship.ResultsThe analysis reveals that DGGHE affects DALY negatively and that governance improves the effect of DGGHE on DALY, with bigger improvements among countries with worse governance.ConclusionThese findings provide evidence that good governance is crucial to the effectiveness of PHS in SSA nations. Sub-Saharan Africa (SSA) countries should improve governance to improve population health.

A two-stage decision-making approach for optimal design and operation of integrated energy systems considering multiple uncertainties and diverse resilience needs

The enhancement of energy system resilience from a planning perspective is crucial. In this study, a two-stage resilience optimization model is developed to determine the optimal additional configuration and scheduling plan for an integrated energy system, taking into account economic costs, loss of load, and various resilience indicators. Considering the uncertainty of disaster occurrence and component vulnerability, probabilities of multiple energy interruption scenarios are introduced, and a comprehensive system configuration optimization model targeting annual energy supply cost and loss of load is developed. Following which, accounting for the personalized energy demands of end-users, three resilience indicators, namely load importance, energy interruption duration, and the maximum degree of system damage, are proposed. According to the simulation results of an illustrative example, the proposed method effectively improve system resilience. Compared to the baseline solution from pure economic perspective, the total cost increases by 0.38 %, while the load loss decreases by 6.84 %. From the demand-side viewpoints, three resilience indicators are improved by 12.17 %, 11.8 %, and 20 %, respectively. Moreover, the capacity of renewable energy is recognized as the most critical factor determining the load loss over the planning period. Storage devices are more beneficial in reducing the duration of energy interruptions and lowering the maximum damage degree.

Infrastructure-assisted 3D detection networks based on camera-lidar early fusion strategy

Target detection in autonomous driving is a pivotal domain of the current research focus. The process fundamentally relies on on-board sensors tasked to identify proximate objects. However, long-range perception instability and the partial obstruction by traffic participants further complicate the challenge. These factors collectively affected the effectiveness of targeted detection from the cooperative vehicle-infrastructure system (CVIS) and urgently need to be addressed. Starting from infrastructure-side assisted detection, we propose a 3D detection network based on multi-sensor sensing. Our approach consists of three sub-networks: Diversity Balanced Feature Fusion Network (MRBNeXt), Early Multimodal Fusion Network (VBRFusion), and TwoStage Lightweight Detection Network (TSL). MRBNeXt focuses on extracting raw images fused into multilevel semantic representations to address the drawbacks of needing a rich feature-level representation; VBRFusion proposes a two-branch structure that acts on the point cloud voxelization to aggregate high-dimensional features. The point features are mapped to the sampled graphical semantic features via the coordinate features to complete the early fusion and thus improve the feature quality of a single modality. In the proposed area network, TSL enhances the sensory field processing of multidimensional features using the context-aware module in a two-stage approach to achieve fast target recognition at different scale levels. Finally, we perform comparative ablation experiments on the DAIR-V2X vehicle-infrastructure dataset. The results validated our approach and demonstrated its effectiveness and enhancement in detection accuracy at the infrastructure end compared to current state-of-the-art methods. This improvement significantly boosted the performance of 3D target detection tasks in complex traffic scenarios and provided a more robust justification for the subsequent development of vehicle-side-infrastructure-side collaborative 3D target detection.

Refined mode classification and performance analysis method of dual-mode scramjet vehicles for flight trajectory optimization

Hypersonic air-breathing vehicles, utilizing ramjet and scramjet propulsion systems, are emerging as a promising option for future hypersonic transportation due to high specific impulse. The performance of hypersonic aircraft is significantly influenced by their flight trajectory, as combustion efficiency and aerodynamic forces vary markedly with altitude. Although numerous studies have focused on optimizing these trajectories, they have not adequately considered the potential for operational failures in the propulsion system, such as unstart and blowout. This study introduces a trajectory optimization approach for hypersonic aircraft that proactively addresses and mitigates these operational failures. This is achieved by establishing an operational classification process for a dual-mode scramjet engine and proposing a failure mode avoidance strategy using a Gaussian process classifier. Optimized ascent trajectories are achieved through the development of a two-stage robust sequential convex programming approach. The results of trajectory optimization indicate that the failure mode avoidance strategy proposed is crucial in obtaining minimal time trajectories, and that the optimal trajectories include drop motion in the initial stage flight in order to increase combustion efficiency.

Decision-Making Approach to Design a Sustainable Photovoltaic Closed-Loop Supply Chain Considering Market Share for Electric Vehicle Energy

This study aims to develop a model for the closed-loop supply chain of photovoltaic (PV) systems. The primary objective addresses strategic and tactical decision-making using a two-stage approach. To pinpoint suitable locations for solar power plants, the PROMETHEE II method is utilized, which is a component of multi-attribute decision making (MADM) approaches. Next, a multi-objective modeling of the closed-loop PV supply chain is conducted. This model aims to minimize total supply chain costs, reduce environmental impacts, mitigate adverse social effects, maximize the on-time delivery (OTD) of manufactured products, and maximize market share. Additionally, a robust fuzzy mathematical model is introduced to examine the model’s sustainability under various uncertainties. An evaluation of the effectiveness and utility of this model is conducted in Tehran city. Furthermore, a comprehensive analysis of various supply chain costs indicates that production centers have the highest costs, while separation centers have the lowest costs.

A Global Mosaic of Temporally Stable Pixels for Radiometric Calibration of Optical Satellite Sensors Using Landsat 8

Calibrating optical sensors has become a priority to maintain data quality and ensure consistency among sensors from different agencies. Achieving and monitoring radiometric calibration often involves the identification of temporally stable targets on the Earth’s surface. Although some locations across North Africa have traditionally been used as primary targets for calibration purposes, it is crucial to explore alternative options to account for potential changes in these sites over time. This study conducted a global assessment of pixel-level temporal stability using Landsat 8 OLI data, with the primary goal of identifying regions suitable for global radiometric calibration efforts. This work followed a two-stage approach, including the testing and selection of an effective combination of statistical tests to differentiate between temporally stable and unstable pixels and the generation of a worldwide mosaic of temporally stable pixels through a per-pixel statistical analysis employing a combination of Spearman’s rho and Pettitt’s test for assessing long-term trends and detecting change points. Notably, comparing the temporal mean top-of-atmosphere (TOA) reflectance before and after applying the generated temporal filter to a site with documented unstable pixels revealed a substantial reduction in mean variation, up to 6%. In addition, slopes observed in the pre-filter mean TOA reflectance, ranging between −0.002 and −0.005, became zero or near-zero and statistically insignificant after the temporal filter was applied, demonstrating a reduction in total uncertainties by 3 to 4%. These findings evidence the potential of this work, placing it as a potential foundation in the continuous search to identify additional targets for global radiometric calibration efforts.

P-751 Development of hypertensive complications in oocyte donation pregnancy: a systematic review and individual participant data meta-analysis (DONOR IPD)

Abstract Study question Is the risk for hypertensive complications, including pregnancy-induced hypertension and preeclampsia, increased in oocyte donation pregnancy compared to autologous oocyte pregnancies? Summary answer We found an approximate 2.5-fold increased risk for hypertensive complications in oocyte donation pregnancy compared to autologous oocyte pregnancy using individual participant data meta-analysis approaches. What is known already Oocyte donation (OD) is an established assisted reproductive technique involving an oocyte donor and recipient. Due to the expansion of indications and emerging trends, the number of OD treatments is rising. Previous meta-analyses highlight increased risks of hypertensive complications compared to naturally conceived (NC) and IVF/ICSI pregnancies, including pregnancy induced hypertension (PIH) and preeclampsia (PE). However, limitations exist due to study quality, lack of correction for confounders, and heterogeneity. Study design, size, duration We conducted the DONation of Oocytes in Reproduction individual participant data (DONOR IPD) meta-analysis to determine the risk for the development of hypertensive complications in OD pregnancy, in comparison to autologous oocyte pregnancy. The DONOR IPD meta-analysis aims to address confounders, enhance statistical power, and provide robust evidence. Participants/materials, setting, methods A literature search was conducted using PubMed, EMBASE and Cochrane up to March 2023. We included observational studies that compared OD pregnancy beyond 20 weeks of gestation with autologous pregnancy, reporting on hypertensive pregnancy complications. Authors of eligible articles asked to share IPD. The DONOR IPD meta-analysis was executed using both a one- and two-stage approach, adjusted for confounders maternal age, parity and multiple gestation. Furthermore, sensitivity, meta-regression and subgroup analyses were performed. Main results and the role of chance IPD was sought for 58 cohorts, and eventually provided from sixteen cohorts with data of 2,747 OD, 4,699 autologous IVF/ICSI, and 33,323 NC pregnancies in total. The one- and two-stage approach comparing OD to autologous pregnancy resulted in adjusted ORs of respectively 2.62 (95% CI 2.22-3.10) and 2.65 (95% CI 2.08-3.39) for hypertensive complications in total, 2.15 (95% CI 1.73-2.68) and 1.49 (95% CI 0.80-2.80) for PIH, and 2.28 (95% CI 1.88-2.78) and 2.39 (95% CI 1.94-2.94) for PE. When the autologous group was split in NC and IVF/ICSI pregnancies, the higher risks in OD pregnancy for hypertensive complications, including PIH and PE, remained. However, compared to autologous pregnancies no significant increase in the risk for HELLP syndrome was observed in OD pregnancy. Limitations, reasons for caution The use of IPD still encounters certain limitations, such as IPD availability bias, and the dependency of methodological quality and heterogeneity of primary studies. To address these issues, we conducted additional sensitivity, meta-regression, and subgroup analyses. Wider implications of the findings Currently, none of the worldwide used guidelines indicate OD as a risk factor for hypertensive complications. As the DONOR IPD meta-analysis results in the best evidence based statement for international guidelines in obstetrics untill now, alertness of health care professionals must be increased towards the risk profile of these pregnancies. Trial registration number Financial support from the Leiden University Medical Center (the Netherlands) for an MD/PhD grant for K. van Bentem.

Elementary education outcome efficiency of Indian state governments: a generalized stochastic frontier approach

PurposeThis paper aims to analyze the technical efficiency of Indian State governments in providing elementary education (EE) and to identify the determinants of their technical inefficiency.Design/methodology/approachThe Generalized Stochastic Frontier Approach (GSFA) is used in the context of the Inefficiency Effects Model to simultaneously estimate the frontier production function and the technical inefficiency model. Panel data of 28 Indian States from 2009–10 to 2018–19 is used.FindingsThe mean efficiency of States stands at 86%. Efficiency varied between 67 and 97%. 96% of the inter-state disparity in EE outcomes can be explained by inefficiency. Arunachal Pradesh is the least efficient State, followed by Sikkim and Tripura. Efficiency estimates were observed to change across States over the study period. Proportion of government schools, rural population, and proportion of Schedule Caste and Schedule Tribe children are the major determinants of inefficiency.Practical implicationsThis study emphasizes that efficient resource management is as important as adequate resource allocation for achieving positive EE outcomes. It distinguishes resource-poor States from inefficient ones, providing insights to enhance States’ efficiency, and aiding policymakers in formulating strategies for ensuring equitable and quality EE.Originality/valueThis is the first paper to apply GSFA (for Indian States), providing a more robust estimation of efficiency. The Inefficiency Effects Model is employed that address the limitations inherent in the two-stage approach.

Security monitoring via sound analysis and voice identification with artificial intelligence

The article demonstrates the possibility of monitoring user access through authentication based on voice profiles using the means of Artificial Intelligence. A two-stage approach is proposed for sound analysis and voice recognition using Feed-Forward Neural Networks (FFNNs) and Cascade-Forward Neural Networks (CFNNs). Seven test voice profiles were pre-processed to extract quantitative sound features. The procedure involves registration of a set of sound parameters concerning three categories, respectively, for all audio and acoustic measurements in the entire sound spectrum, measurements up to and above 100 dBA. The neural architectures were trained with Scaled Gradient Descent (SCG) and Levenberg Marquardt (LM) algorithms, using different transfer functions in the output structural layers. In the initial phase of neural training, the entire sound spectrum of registered indicators was used, and high levels of Accuracy around 90.0% were reached. Subsequently, steps were taken to reduce the informative features when searching for similar levels of accuracy in order to limit the necessary computational procedures in neural training, but maintain the threshold of successful user authentication. In the analysis of neural performance, in addition to accuracy, additional criteria were used, namely Mean-Squared Error (MSE) and Root Mean Squared Error (RMSE). About the achieved and analyzed results, a synthesis was conducted of a set of four informative features with the highest significance, respectively LAE (A-weighted, sound exposure level), Laeq (A-weighted, equivalent sound level), LAF (A-weighted, fast time-constant, sound level) and LAS (A-weighted, slow time constant response, sound level). In the course of subsequent neural training processes, unsuitability was found when using the Log-sigmoid activation type with greatly underestimated accuracy readings and errors below 58.0% and above thresholds of 0.2300 and 0.4800. Positive performance indicators of voice recognition were achieved with Softmax and Hyperbolic tangent sigmoid activations in SCG and LM training procedures in levels of accuracy of 98.7 % and 96.1 % at FFNN models. Successful correct recognition of the test voice profiles on access and security personalization with a quantitative equivalent of 100.0 % accuracy was achieved in the Linear transfer function for Cascade-Forward Neural Networks. The proposed method and the synthesized neural models in the research can be used as units and modules in access control systems with biometric diagnostics and intelligent recognition of employees in company departments to electronically store classified information and physical access control.

Explicit computational analysis of unsteady maxwell nanofluid flow on moving plates with stochastic variations

It is imperative to investigate the proposed computational scheme for the resolution of two-dimensional partial differential equations that result from non-Newtonian nanofluid flow over flat and oscillatory sheets, taking into account the effects of the magnetic field and chemical reactions, as it is designed to address stochasticity, unstable flow conditions, and Maxwellian behaviour. Consequently, it offers valuable insights into the dynamics of nanofluids. A stochastic computational scheme is suggested to address the two-dimensional partial differential equations (PDEs) caused by non-Newtonian nanofluid flow over flat and oscillatory sheets in the presence of a magnetic field and chemical reaction effects. The Taylor series analysis is employed to construct a scheme. The two-stage approach can be employed to discretize the time derivative in the differential equations. The stability study results and the consistency by mean square measure are also included. The continuity equation in the given partial differential equations (PDEs) is discretized using the first-order finite difference approach. The remaining equations, including the energy equation, the nanoparticle volume fraction, and the Navier-Stokes equation, are discretized using the second-order difference in space scheme that is proposed. Three separate figures show how the various parameters affect the nanoparticle volume fractions, velocity, and temperature. Through a focus on a specific computational technique designed to handle the challenges given by stochasticity, unstable flow conditions, and the Maxwellian behaviour shown by these nanofluids, this study introduction serves as a portal into the involved domain of nanofluid dynamics.

Transit fares integrating alternative modes as a delay insurance

Public transport (PT) fare policy remains subject to innovations, not least evident in the Mobility as a Service discussion. Mode integration and related fare strategies can be used to increase the attractiveness of PT by compensating for potential delays. This study proposes “premium fares” as a novel pricing tool that can evaluate and improve the travel time reliability on a multimodal transportation network. The premium fare is higher than the standard fare but allows passengers to use an alternative service free of charge if waiting for the delayed public transport service is anticipated to be longer than a certain qualification threshold. Properties that guarantee monotonicity of the premium fare with respect to distance traveled are developed. The operator aims to find the premium fare price and qualification threshold that can maximize its profit, based on the probability distributions of delay and passengers’ value of time (VOT). We model this optimization problem for a railway line given a limited capacity of alternative mode services, e.g., the number of taxis. A two-stage approach using nonlinear and dynamic programming is developed to obtain the optimal decision variables and associated capacity allocation plan. Our results show that the introduction of the premium fare can benefit both operators and travelers with increased profits and improved travel time reliability. The interplay between fares and the qualification threshold is illustrated using various delay and VOT distributions. To attract enough customers the premium fare has to be set below a specific level dependent on the VOT distribution. Meanwhile, the operator adjusts the qualification threshold to control the cost paid to the alternative service provider.

Striving towards robust phase diversity on-sky

Context. The recent IRLOS upgrade for VLT/MUSE narrow field mode (NFM) introduced a full-pupil mode to enhance sensitivity and sky coverage. This involved replacing the 2 × 2 Shack-Hartmann sensor with a single lens for full-aperture photon collection, which also enabled the engagement of the linearized focal-plane technique (LIFT) wavefront sensor instead. However, initial on-sky LIFT experiments have highlighted a complex point spread function (PSF) structure due to strong and polychromatic non-common path aberrations (NCPAs), complicating the accurate retrieval of tip-tilt and focus using LIFT. Aims. This study aims to conduct the first on-sky validation of LIFT on VLT/UT4, outline challenges encountered during the tests, and propose solutions for increasing the robustness of LIFT in on-sky operations. Methods. We developed a two-stage approach to focal-plane wavefront sensing, where tip-tilt and focus retrieval done with LIFT is preceded by the NCPA calibration step. The resulting NCPA estimate is subsequently used by LIFT. To perform the calibration, we proposed a method capable of retrieving the information about NCPAs directly from on-sky focal-plane PSFs. Results. We verified the efficacy of this approach in simulated and on-sky tests. Our results demonstrate that adopting the two-stage approach has led to a significant improvement in the accuracy of the defocus estimation performed by LIFT, even under challenging low-flux conditions. Conclusions. The efficacy of LIFT as a slow and truth focus sensor in practical scenarios has been demonstrated. However, integrating NCPA calibration with LIFT is essential to verifying its practical application in the real system. Additionally, the proposed calibration step can serve as an independent and minimally invasive approach to evaluate NCPA on-sky.

Holiday Peak Load Forecasting Using Grammatical Evolution-Based Fuzzy Regression Approach

Peak load forecasting plays an important role in electric utilities. However, the daily peak load forecasting problem, especially for holidays, is fuzzy and highly nonlinear. In order to address the nonlinearity and fuzziness of the holiday load behaviors, a grammatical evolution-based fuzzy regression approach is proposed in this paper. The proposed hybrid approach is based on the theorem that fuzzy polynomial regression can model all fuzzy functions. It employs the rules of the grammatical evolution to generate fuzzy nonlinear structures in polynomial form. Then, a two-stage fuzzy regression approach is used to determine the coefficients and calculate the fitness of the fuzzy functions. An artificial bee colony algorithm is used as the evolution system to update the elements of the grammatical evolution system. The process is repeated until a fuzzy model that best fits the load data is found. After that, the developed fuzzy nonlinear model is applied to forecast holiday peak load. Considering that different holidays possess different load patterns, a separate forecaster model is built for each holiday. Test results on real load data show that an averaged absolute percent error less than 2% can be achieved, which significantly outperforms existing methods involved in the comparison.

Modified median quartile double ranked set sampling for estimation of population mean

Environmental monitoring and assessment aim to gather data economically, without bias, using efficient and cost-effective sampling methods. One such traditional method is Ranked Set Sampling (RSS), often employed to achieve observational economy. This article introduces an innovative two-stage sampling approach for ranked set sampling (RSS) to get a more precise estimate of the population mean. Modified Median Quartile Double Ranked Set Sampling (MMQDRSS) highlights the ranked base technique's potential as a cost-effective sampling method. To evaluate the performance of the proposed estimator by using real-life data and conducting a simulation study to compare the relative efficiency of the proposed estimator with some existing methods.

The causal impact of shared decision making on pain outcomes: Gender matters

This national US study determined the causal impact of shared decision making (SDM) on pain outcomes, including any overall pain and the subcategories of any acute pain and any chronic pain. We additionally examined whether the causal impact of SDM on overall pain is moderated by gender, race-ethnicity, clinician-patient racial-ethnic concordance, and clinician-patient gender concordance. We used national US data from the 2003–2017 Medical Expenditure Panel Survey, which were externally valid to the US national non-institutionalized population, employed a standard measure of shared decision making, and applied an internally-valid two-stage least squares approach that used the peer SDM behavior of similar clinicians as an instrument. The instrument was sufficiently strong and statistically uncorrelated with patient characteristics. We found a large impact of SDM on both female and male chronic pain outcomes, where the effect for females was approximately 50% larger than for males, with a 10 percentage point increase in SDM quality resulted fewer females experiencing chronic pain that interfered with everyday activities (−24.8 percentage points; 95% confidence interval [CI]: 43.3, −6.4) than males (−16.5 percentage points; 95% CI: 32.9, −1.0). We estimated that a 10 percentage point national increase in the SDM index would thus result in 10.1 million fewer females and 5.7 million fewer males in the US experiencing chronic pain that interfered with their daily activities. Policy implications include both increasing the quality of SDM for all patients and educating male patients on the value of SDM.

Shehata technique versus Fowler-Stephens orchidopexy in intra-abdominal testis: A meta-analysis

IntroductionThe management of intra-abdominal testis (IAT) represents a significant clinical challenge, necessitating the transposition of the testis from the abdominal cavity to the scrotum. This procedure is rendered complex by the abbreviated length of the testicular vessels. ObjectiveOur purpose in this study was to conduct a systematic review and meta-analysis comparing Shehata technique (ST) versus Fowler Stephens technique (FST) in treating patients with IAT. Study designWe conducted a comprehensive literature search using several databases, including Ovid Medline, Cochrane, PubMed, Google Scholar, Web of Sciences, EMBASE, and SCOPUS until February 2024. This study included research that compared ST and FST for managing intra-abdominal testis. We evaluated the rates of atrophy and retraction, as well as the overall success rates, for both techniques. ResultsSix studies were identified as appropriate for meta-analysis, comparing orchidopexy performed using the ST with 169 patients, against the FST involving 162 patients. The comparison showed no statistically significant age difference at the time of surgery between the groups (I2 = 0%), (WMD 0.05, 95% CI − 1.24 to 1.34; p=0.94). Operative time in first the stage was lower in the FST group than ST group (I2 = 95%), (WMD 10.90, 95% CI 1.94 to 19.87; p=0.02). Operative time in the second stage was lower in the ST group than FST group (I2 = 83%), (WMD - 6.15, 95% CI - 12.21 to -0.10; p=0.05). Our analysis showed that ST had a similar atrophy rate (I2=0%) (OR: 0.45, 95% CI: 0.20 to 1.01; p=0.05). No difference was found between techniques in terms of retraction rate (I2=0%) (OR: 0.64, 95% CI: 0.17 to 2.47; p=0.52). The ST demonstrated a notably higher overall success rate compared to FST (I2=1%) (RR: 1.14, 95% CI: 1.03 to 1.27; p=0.009). Overall success rate in ST and FST were 87% and 74%, respectively. Overall atrophy rate in ST and FST were 5% and 12%, respectively. Overall retraction rate in ST and FST were 5% and 10%, respectively. DiscussionThe ST, renowned for its pioneering two-stage laparoscopic approach that leverages mechanical traction to lengthen the testicular vessels, is gaining popularity due to its recognized safety and efficacy. Conversely, the Fowler-Stephens technique, a traditional method that relies on collateral blood supply for testicular mobilization, has come under examination for its potential link to an increased risk of testicular atrophy. ConclusionThis meta-analysis reveals that the Shehata technique has similar or better outcomes compared to the Fowler-Stephens technique in IAT management. Further prospective multicentric randomized controlled trials are warranted.

Deep Learning-based Intelligent Fault Diagnosis for Power Distribution Networks

Power distribution networks with distributed generation (DG) face challenges in fault diagnosis due to the high uncertainty, randomness, and complexity introduced by DG integration. This study proposes a two-stage approach for fault location and identification in distribution networks with DG. First, an improved bald eagle search algorithm combined with the Dijkstra algorithm (D-IBES) is developed for fault location. Second, a fusion deep residual shrinkage network (FDRSN) is integrated with IBES and support vector machine (SVM) to form the FDRSN-IBS-SVM model for fault identification. Experimental results showed that the D-IBES algorithm achieved a CPU loss rate of 0.54% and an average time consumption of 1.70 seconds in complex scenarios, outperforming the original IBES algorithm. The FDRSN-IBS-SVM model attained high fault identification accuracy (99.05% and 98.54%) under different DG output power levels and maintained robustness (97.89% accuracy and 97.54% recall) under 5% Gaussian white noise. The proposed approach demonstrates superior performance compared to existing methods and provides a promising solution for intelligent fault diagnosis in modern distribution networks.