Ranked Probability Research Articles

Specification Choices in Quantile Regression for Empirical Macroeconomics

ABSTRACTQuantile regression has become widely used in empirical macroeconomics, in particular for estimating and forecasting tail risks. This paper examines various choices in the specification of quantile regressions for macro applications, including how and to what extent to include shrinkage and whether to apply shrinkage in a classical or Bayesian framework. We focus on forecasting accuracy, measured with quantile scores and quantile‐weighted continuous ranked probability scores at a range of quantiles from the left to right tail. Across applications, we find that shrinkage is generally helpful to quantile forecast accuracy, with Bayesian quantile regression dominating frequentist quantile regression.JEL Classification: C53, E17, E37, F47

Photometric Redshift Estimation of Quasars by a Cross-modal Contrast Learning Method

Estimating photometric redshifts (photo-z) of quasars is crucial for measuring cosmic distances and monitoring cosmic evolution. While numerous point estimation methods have successfully determined photo-z, they often struggle with the inherently ill-posed nature of the problem and frequently overlook significant morphological features in the probability density functions (pdfs) of photo-z, such as calibration and sharpness. To address these challenges, we introduce a cross-modal contrastive learning probabilistic model that employs adversarial training, contrastive loss functions, and a mixture density network to estimate the pdf of photo-z. This method facilitates the conversion between multiband photometric data attributes, such as magnitude and color, and photometric image features, while extracting features invariant across modalities. We utilize the continuous ranked probability score (CRPS) and the probability integral transform (PIT) as metrics to assess the quality of the pdf. Our approach demonstrates robust performance across various survey bands, image qualities, and redshift distributions. Specifically, in a comprehensive data set from the Sloan Digital Sky Survey and the Wide-field Infrared Survey Explorer (WISE) survey, our probabilistic model achieved a CRPS of 0.1187. Additionally, in a combined data set from SkyMapper and WISE, it reached a CRPS of 0.0035. Our probabilistic model also produced well-calibrated PIT histograms for both data sets, indicating nearly uniform distributions. We further tested our approach in classification tasks within the SkyMapper data set. Despite the absence of u, v, and g bands, it effectively distinguished between quasars, galaxies, and stars with an accuracy of 98.96%. This versatile method can be extended to other scenarios, such as analyzing extended sources like galaxies, across different surveys and varying redshift distributions.

Different minimally invasive surgical methods to hysterectomy for benign gynecological disease: A systematic review and network meta-analysis.

This network meta-analysis aimed to compare the perioperative efficacy of various minimally invasive hysterectomy procedures for treating benign gynecological diseases and to assess whether vaginal natural orifice transluminal endoscopic hysterectomy (VNOTEH), a recently emerging procedure, is inferior to traditional laparoscopy. We searched PubMed, Cochrane Library, Embase, China National Knowledge Infrastructure (CNKI), China Biology Medicine disc (CBM), Wanfang Data, and China VIP Database from inception to August 2022 and updated in June 2023. We included randomized controlled trials (RCTs) comparing different minimally invasive hysterectomy techniques in patients with benign gynecological conditions. The intervention measures included nine minimally invasive hysterectomies. The two researchers used the Cochrane risk-of-bias assessment tool for study appraisal. All statistical analyses and drawings were performed using STATA 17.0 and R 4.4.1. A network meta-analysis (NMA) was conducted to compare the effectiveness of minimally invasive hysterectomy and rank its relative impact probabilistically. A total of 78 RCTs involving 7640 patients and nine minimally invasive hysterectomy methods with 16 intervention combinations were included in this study. Among these, 2, 63, and 13 studies were deemed to have a low, medium, and high risk of bias, respectively. Based on the Surface Under the Cumulative Ranking (SUCRA) probability ranking results of NMA, laparoendoscopic single-site surgery-laparoscopic-assisted vaginal hysterectomy (LESS-LAVH) demonstrated superior outcomes in terms of complications, infections, and 24-h postoperative pain scores. LAVH exhibited better performance in injuries and hospital stays, total laparoscopic hysterectomy showed the least blood loss, and vaginal hysterectomy had the shortest operation time. LESS-LAVH and LAVH are recommended options, if feasible. Meanwhile, VNOTEH can achieve comparable results to traditional laparoscopy but requires careful attention to the risk of injury and infection. Future research should aim to broaden the search scope by including high-quality, large-scale, multicenter RCTs.

Non-stationary spatio-temporal modeling using the stochastic advection-diffusion equation

We construct flexible spatio-temporal models through stochastic partial differential equations (SPDEs) where both diffusion and advection can be spatially varying. Computations are done through a Gaussian Markov random field approximation of the solution of the SPDE, which is constructed through a finite volume method. The new flexible non-separable model is compared to a flexible separable model both for reconstruction and forecasting, and evaluated in terms of root mean square errors and continuous rank probability scores. A simulation study demonstrates that the non-separable model performs better when the data is simulated from a non-separable model with diffusion and advection. Further, we estimate surrogate models for emulating the output of a ocean model in Trondheimsfjorden, Norway, and simulate observations of autonomous underwater vehicles. The results show that the flexible non-separable model outperforms the flexible separable model for real-time prediction of unobserved locations.

Comparative efficacy of colchicine and intensive LDL-C lowering in patients with ASCVD receiving statins: a network meta-analysis of randomised controlled trials

Abstract Background Adding intensive low-density lipoprotein cholesterol (LDL-C) lowering treatments or colchicine to statins resulted in additional cardiovascular benefits. However, the relative efficacy and priority of these agents are unclear. Purpose We aimed to compare the effects of these supplementary agents on cardiovascular outcomes in patients with atherosclerotic cardiovascular diseases (ASCVD) receiving statins. Methods We performed a systematic review and frequentist network meta-analysis on randomized controlled trials. The primary efficacy endpoint was the main adverse cardiovascular event (MACE), and the secondary efficacy endpoints were myocardial infarct, stroke, coronary revascularization, cardiovascular death and all-cause mortality. The safety endpoints were treatment discontinuation and non-cardiovascular death. Colchicine was used as reference treatment. Random-effects model was used in case of high heterogeneity, the fixed-effects model was preferred otherwise. We ranked the comparative effects of all drugs with surface under the cumulative ranking (SUCRA) probabilities. Results 17 trials totaling 85823 participants treated with colchicine, proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor, Niemann-Pick C1-like 1 protein (NPC1L1) inhibitor and ATP citrate lyase (ACL) inhibitor were included. Compared with colchicine, NPC1L1 inhibitor was associated with increased risk of MACE (risk ratio [RR]: 1.39, 1.21-1.59), stroke (RR: 1.81, 1.09-3.00) and coronary revascularization (RR: 1.36, 1.11-1.68), and PCSK9 inhibitor was associated with increased risk of MACE (RR: 1.27, 1.10-1.46). However, NPC1L1 inhibitor (RR: 0.68, 0.48-0.96) and PCSK9 inhibitor (RR: 0.64, 0.44-0.93) were associated with reduced risk of non-cardiovascular death in comparison to colchicine. PCSK9 inhibitors had a lower risk of MACE (RR: 0.91, 95% CI: 0.85-0.98) and coronary revascularization (RR: 0.89, 95% CI: 0.81-0.97) compared with NPC1L1 inhibitor. Although no regimen prolonged survival, colchicine had the lowest rank at non-cardiovascular death and the worse performance on all-cause mortality. Conclusions In patients with ASCVD receiving statins, inflammation-inhibiting with colchicine seems to be superior to intensive LDL-C lowering therapy with PCSK9 inhibitor or NPC1L1 inhibitor in cardiovascular prevention. However, using colchicine as an alternative to intensive lipid-lowering therapy may need to be weighed against the greater cardiovascular benefits and the potential harms of higher non-cardiovascular death. Given the worse performance of colchicine on survival based on current evidence, colchicine may be more appropriate for patients who have been treated with intensive LDL-C-lowering agents but still have recurrent cardiovascular events.Forest plots of included agentsRadar plot of treatments based on SUCRA

Comparison of Surgical Techniques in Managing Craniosynostosis: Systematic Review and Bayesian Network Meta-Analysis

Open total cranial vault reconstruction (CVR) is the common procedure in managing craniosynostosis, yet more techniques have been introduced as alternatives, namely endoscopic suturectomy (ES), endoscopy-assisted craniectomy (EC), spring-assisted surgery (SAS), strip craniectomy with helmet (SC), Pi craniectomy (PiC), Pi plasty (PiP), and Renier’s “H” technique (RH). The aim of this study was to compare the effectiveness of craniosynostosis surgeries in improving the cephalic index of the patients. Studies published until 7 March 2024 reporting CVR, ES, SAS, SC, RH, and PiP as definitive craniosynostosis management with the cephalic index as the outcome were included. Bayesian network meta-analysis and pair-wise meta-analysis were performed using a random-effects model based on standardized mean difference (SMD) and 95% confidence interval (CI). Nine studies published in 2008–2024 recruiting a total of 464 craniosynostosis patients (age: 18–61 months) were included in this meta-analysis. EC (SMD = 0.23 [95%CI: −5.47 to 5.63]; p = 0.935), PiP (SMD = −0.07 [95%CI: −9.27 to 8.79]; p = 0.988), ES (SMD = −0.59 [95%CI: −6.07 to 4.94]; p = 0.834), PiC (SMD = −1.16 [95%CI: −8.89 to 6.35]; p = 0.765), RH (SMD = −0.96 [95%CI: −6.62 to 4.53]; p = 0.736), SAS (SMD = −0.86 [95%CI: −8.25 to 6.18]; p = 0.815), and SC (SMD = −1.79 [95%CI: −9.05 to 5.28]; p = 0.624) were found to be as effective as CVR in improving the cephalic index. Network meta-analysis suggests that PiP is the most effective among these techniques (rank 1 probability = 0.273). According to the rank probabilities of our model the order of techniques from the most to the least effective is as follows: EC > CVR > PiP > ES > SAS > RH > PiC > SC.

Applying MCDM to evaluate benchmark scores in the logistics sector for the period 2021–2023: Application to universities in Vietnam

Choosing a university is a crucial decision for each field of study, as it significantly influences the quality of graduates. An important factor in this decision is the university’s annual benchmark scores. The benchmark score represents the minimum score required for admission. This study evaluates the benchmark scores in the logistics sector for several prominent universities in Vietnam during the period 2021–2023. The research process utilized data on the benchmark scores for the years 2021, 2022, and 2023. The weights of these benchmark scores were calculated using the Rank Order Centroid (ROC) method, and the Probability method was employed to compare the benchmark scores of the universities. The analysis identified C3 as the criterion with the highest importance, while U3 emerged as the top-ranked alternative. The two-stage comprehensive sensitivity analysis revealed that universities consistently ranked high or low regardless of the method used to calculate benchmark score weights or the method employed for ranking. Additionally, the smallest weight change that affected the overall Probability ranking was 4.61%. This study provides significant guidance for students in selecting a university for logistics studies and serves as a foundational reference for universities to assess their capabilities in logistics education, thereby fostering healthy competition among institutions.

Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Models

Latent Gaussian process (GP) models are flexible probabilistic non-parametric function models. Vecchia approximations are accurate approximations for GPs to overcome computational bottlenecks for large data, and the Laplace approximation is a fast method with asymptotic convergence guarantees to approximate marginal likelihoods and posterior predictive distributions for non-Gaussian likelihoods. Unfortunately, the computational complexity of combined Vecchia-Laplace approximations grows faster than linearly in the sample size when used in combination with direct solver methods such as the Cholesky decomposition. Computations with Vecchia-Laplace approximations can thus become prohibitively slow precisely when the approximations are usually the most accurate, i.e., on large data sets. In this article, we present iterative methods to overcome this drawback. Among other things, we introduce and analyze several preconditioners, derive new convergence results, and propose novel methods for accurately approximating predictive variances. We analyze our proposed methods theoretically and in experiments with simulated and real-world data. In particular, we obtain a speed-up of an order of magnitude compared to Cholesky-based calculations and a threefold increase in prediction accuracy in terms of the continuous ranked probability score compared to a state-of-the-art method on a large satellite data set. All methods are implemented in a free C++ software library with high-level Python and R packages. 4

Different peripheral nerve blocks for patients undergoing total knee arthroplasty: a network meta-analysis of randomized controlled trials.

To comprehensively compare the effect of different peripheral nerve blocks for patients undergoing total knee arthroplasty (TKA). PubMed, Embase, Cochrane Library, and Web of Science were comprehensively searched. The outcomes included postoperative pain, postoperative function, adverse events, oral morphine equivalent (OME), and perioperative indicators. Network plots, forest plots, league tables and rank probabilities were drawn for all outcomes. Totally 30 studies were included. For postoperative pain, continuous adductor canal block (cACB) + genicular nerve block (GNB) was most likely to be the most effective block regarding rest pain score at 24h; cACB + GNB was most likely to result in the lowest rest pain score at 48h; patients undergoing cACB + infiltration between the popliteal artery and the capsule of the knee (IPACK) + GNB was most likely to have the lowest motion pain score at 24h; patients undergoing cACB + GNB was most likely to have the lowest motion pain score at 48h. For postoperative function, patients undergoing cACB + IPACK + GNB had the highest likelihood to exhibit the shortest time in Timed Up and Go test (TUG); cACB + tibial nerve block (TNB) was most likely to be the most effective block in terms of range of motion (ROM); cACB + IPACK was most likely to be the optimal block concerning the ambulation distance. cACB combined with IPACK/GNB may be the most favorable block after TKA, continuous blocks may be better than single-shot blocks, and combined blocks may be better than separate blocks.

Low-density Lipoprotein Cholesterol Reduction Therapies for Secondary Prevention in Patients with Stroke: A Network Meta-analysis.

Patients with previous strokes are at a higher risk of stroke recurrence. Current guidelines recommend a range of low-density lipoprotein cholesterol (LDL-C)-lowering treatments to reduce the risk of recurrent stroke. However, the optimal agent for decreasing LDL-C to lower the risk of recurrent stroke remains unclear. This study aimed to assess the relative effects of various LDL-C -lowering agents for secondary stroke prevention. Several databases were searched from inception up to 2022. Only randomized controlled trials that compared different LDL-C-lowering agents in adult patients with previous strokes were included. The primary endpoint was a recurrent stroke. The surface under the cumulative ranking curve (SUCRA) was also applied to estimate the overall ranking probability of the treatment agents for each outcome. Overall, nine trials comprising 17,226 patients were included. Ezetimibe plus statins (RR: 0.56, 95% CrI: 0.35-0.87) and statins alone (RR: 0.90, 95% CrI: 0.81-1.00) reduced the risk of stroke recurrence. Ezetimibe plus statins was superior to statins alone in decreasing the incidence of recurrent stroke (RR: 0.62, 95% CrI: 0.39-0.95). However, treatment with statins was related to an increased risk of hemorrhagic stroke compared to placebo (RR: 1.57, 95% CrI: 1.13-2.21). All agents were related to a decreased incidence of major adverse cardiovascular events. Treatment with ezetimibe plus statins was suggested as the most efficacious in decreasing the incidence of recurrent stroke. The analysis also revealed that statin monotherapy was related to an increased risk of hemorrhagic stroke.

Effects of wearable device training on upper limb motor function in patients with stroke: a systematic review and meta-analysis.

To evaluate the effect of wearable device training on improving upper limb motor function in patients who experienced strokes. The PubMed, Embase, Cochrane Library, Web of Science, MEDLINE, SCOPUS, China National Knowledge Infrastructure, WanFang, and VIP databases were searched for randomized controlled trials (RCTs) that assessed the effectiveness of wearable device training in improving upper limb motor function in patients with stroke. Two investigators independently screened studies by their titles and abstracts and cross-checked, downloaded, and evaluated the results. Disagreements were resolved by a third highly experienced researcher. Risk of bias was evaluated using the Cochrane risk-of-bias tool. This meta-analysis was registered in PROSPERO (registration No. CRD42023421633). This study comprised 508 patients from 14 RCTs. The experimental group assessed various wearable devices, including 3D-printed dynamic orthoses, inertial measurement unit (IMU) sensors, electrical stimulation devices, and virtual reality (VR) devices for virtual interactive training. The control group received traditional rehabilitation therapies, including physical and conventional rehabilitation. The experimental group scored better on the Fugl-Meyer Assessment (FMA-UE) scale (standardized mean difference [SMD] 0.26, 95% confidence interval [CI] 0.07, 0.45) and Box and Block Test (BBT) (SMD 0.43, 95% CI 0.17, 0.69) versus controls. No significant intergroup differences were observed in the Action Research Arm Test (SMD 0.20, 95% CI -0.15, 0.55), motor activity log (mean difference [MD] 0.32, 95% CI -0.54, 0.33), and modified Ashworth scale (MD -0.08, 95% CI -0.81, 0.64). The probability rankings of wearable devices that improved FMA-UE scores in patients with stroke were: orthotic devices, with the highest probability ranking of 0.45, followed by sensor devices at 0.23, electrical stimulation devices at 0.21, and VR devices at 0.11. Wearable device training was found to significantly improve upper limb motor function in patients with stroke, particularly for large-range movements. Improvements in FMA-UE and BBT scores reflected reduced impairment and enhanced manual dexterity, respectively. However, the training had no significant effect on hand movement frequency, fine motor skills, or spasticity. Among the different wearable devices tested, orthoses produced the most effective results.

ProInfer: inference of binary protocol keywords based on probabilistic statistics

Abstract Protocol reverse engineering is crucial in normative verification, and malware behavior analysis and vulnerability discovery. However, uncovering the structural features of binary protocols concealed within dense data representations remains a significant challenge. Accurately identifying keyword segments associated with message types is a prerequisite for meaningful semantic analysis and protocol state machine reduction. In this work, we introduce a novel approach for inferring keywords from binary protocols based on probabilistic statistics. Our method in terms of Byte employs heuristic rules to filter offset positions that are clearly unrelated to message types. We further filter candidate Byte-offsets utilizing constraint relations and provide the probabilistic ranking of each offset as the keyword segment. To enhance the reliability of keyword segment inference, we utilize the Monte Carlo algorithm to assess the difference between message clustering with candidate Byte-offset and random message clustering, and reorder candidate offsets according to the results. Then we can observe optimal values from both orderings and present the ultimate inference results. Experimental results demonstrate that our method excels in the accuracy of keyword segments identification compared with previous techniques.

Correction to: An experiment with ANNs and Long‑Tail Probability Ranking to Obtain Portfolios with Superior Returns

Correction to: An experiment with ANNs and Long‑Tail Probability Ranking to Obtain Portfolios with Superior Returns

Comparing the effectiveness of environmental DNA and camera traps for surveying American mink (Neogale vison) in northeastern Indiana.

While camera traps can effectively detect semi-aquatic mammal species, they are also often temporally and monetarily inefficient and have a difficult time detecting smaller bodied, elusive mammals. Recent studies have shown that extracting DNA from environmental samples can be a non-invasive, alternative method of detecting elusive species. Environmental DNA (eDNA) has not yet been used to survey American mink (Neogale vison), a cryptic and understudied North American mustelid. To help determine best survey practices for the species, we compared the effectiveness and efficiency of eDNA and camera traps in surveys for American mink. We used both methods to monitor the shoreline of seven bodies of water in northeastern Indiana from March to May 2021. We extracted DNA from filtered environmental water samples and used quantitative real-time PCR to determine the presence of mink at each site. We used Akaike's Information Criterion to rank probability of detection models with and without survey method as a covariate. We detected mink at four of the seven sites and seven of the 21 total survey weeks using camera traps (probability of detection (ρ) = 0.36). We detected mink at five sites and during five survey weeks using eDNA (ρ = 0.25). However, the highest probability of detection was obtained when both methods were combined, and data were pooled (ρ = 0.47). Survey method did not influence model fit, suggesting no difference in detectability between camera traps and eDNA. Environmental DNA was twice as expensive, but only required a little over half (58%) of the time when compared to camera trapping. We recommend ways in which an improved eDNA methodology may be more cost effective for future studies. For this study, a combination of both methods yielded the highest probability for detecting mink presence.

Does patient-specific instrument or robot improve imaging and functional outcomes in unicompartmental knee arthroplasty? A bayesian analysis.

This study conducted a Bayesian network meta-analysis (NMA) to compare the imaging and functional outcomes of patient-specific instrument-assisted unicompartmental knee arthroplasty (P-UKA), robot-assisted unicompartmental knee arthroplasty (R-UKA), and conventional unicompartmental knee arthroplasty (C-UKA). A comprehensive search was performed on five electronic databases and major orthopedic journals as of September 24, 2023. We included randomized controlled studies featuring at least two interventions of P-UKA, R-UKA, or C-UKA. Primary outcomes encompassed the deviation angle of hip-knee-ankle angle, as well as the coronal and sagittal plane alignment of femoral and tibial components. Secondary outcomes included patient-reported outcome measures (PROM), surgery time, revision rate, and complication rate. Bayesian framework was employed for risk ratio (RR) or mean deviation (MD) analysis, and treatment hierarchy was established based on rank probabilities. This NMA included 871 knees from 12 selected studies. In sagittal plane, R-UKA exhibited a significantly reduced deviation angle of femoral component compared to P-UKA (MD: 4.16, 95% CI: 0.21, 8.07), and of tibial component in comparison to C-UKA (MD: -2.45, 95% CI: -4.20, -0.68). Notably, the surgery time was significantly longer in R-UKA than in C-UKA (MD: 15.98, 95% CI: 3.11, 28.88). However, no significant differences were observed in other outcomes. Compared with P-UKA or C-UKA, R-UKA significantly improves the femoral and tibial component alignment in the sagittal plane, although this does not translate into discernible differences in functional outcomes. Comprehensive considerations of economic and learning costs are imperative for the judicious selection of the appropriate procedure.

Comparative efficacy of non-pharmacological interventions for anxiety in adult intensive care unit patients: A systematic review and network meta-analysis.

The primary goal of the intensive care unit is to the anxiety of conscious patients is often ignored in the care unit. The purpose of this study was to assess the efficacy of various non-pharmacological therapies for anxiety disorders in adult patients in the intensive care unit, in order to enhance humanistic care in the intensive care unit and to promote the patients' physical and mental recovery together. We conducted a systematic and comprehensive search of the literature in five databases (including the Cochrane Library, PubMed, EBSCO, Web of Science, and Embase) covering nearly a decade for randomized controlled trials of non-pharmacological therapies to reduce anxiety in adult intensive care unit patients. Two researchers independently assessed the quality of the literature, collected and condensed the data, and used STATA software to perform a network meta-analysis. The ranking probabilities for each intervention were calculated using the Surface under the Cumulative Ranking (SUCRA) method. The study protocol was registered with PROSPERO. This study ultimately included 26 randomized controlled trials involving 2791 adult ICU patients. Non-pharmacological interventions for anxiety in adult ICU patients included music therapy, aromatherapy, ICU diary, virtual reality, massage therapy, monitoring room diary, and health education. when compared to the control group (usual care), aromatherapy + music therapy [MD = -2.65, 95% CI (-4.76, -0.54)] (P = 0.0137) and music therapy [MD = -1.77, 95% CI (-3.40, -0.13)] (P = 0.0338) were superior in reducing anxiety in adult ICU patients. The results of the network meta-analysis showed that aromatherapy combined with music therapy significantly alleviated anxiety in adult ICU patients (SUCRA: 99.8%). Music therapy combined with aromatherapy has demonstrated superior effectiveness compared to other non-pharmacological interventions for reducing anxiety in awake adults in the ICU. However, the underlying mechanisms of this combined therapy require further exploration. Future research on the use of music therapy combined with aromatherapy in the care unit may help reduce anxiety in patients while fostering their physical and mental healing; however, individual variances and unique clinical circumstances must be considered.

Probabilistic simulation of electricity price scenarios using Conditional Generative Adversarial Networks

A novel approach for generative time series simulation of electricity price scenarios is presented. A “Time Series Simulation Conditional Generative Adversarial Network” (TSS-CGAN) generates short-term electricity price scenarios. In particular, the network is capable of generating a 24-dimensional output vector that corresponds to the expected behavior of electricity markets. The model can replace typical approaches from financial mathematics like statistical factor models to model the price distribution around a given forecast. The data cover a 3-year period from 2020 to 2023. Our empirical study is conducted on the EPEX SPOT market in Europe. An electricity price scenario includes the prices of the hourly contracts of a day-ahead auction at the EPEX SPOT power exchange. The model uses multivariate time series as input factors, consisting of point forecasts of electricity prices and fundamental data on generation and load profiles. The architecture of a TSS-CGAN is based on the idea of Conditional Generative Adversarial Networks combined with 1D Convolutional Neural Networks and Bidirectional Long Short-Term Memory. The model is evaluated using qualitative and quantitative criteria. For the evaluation, 10,000 simulations of a test period are carried out. Qualitative criteria are whether the model follows certain electricity market-specific regularities and depicts them adequately. The quantitative analysis includes common error metric, compared to benchmark models, like DeepAR, Prophet and Temporal Fusion Transformer, the examination of the quantile ranges, the error distribution and a sensitivity analysis. The results show that the TSS-CGAN outperforms benchmark models such as DeepAR by reducing the continuous ranked probability score by 50% and considers market-specific circumstances such as the production of fluctuating energies and reacts correctly to changes in the corresponding variables.

Comparing the efficacy of exercise therapy on adult flexible flatfoot individuals through a network meta-analysis of randomized controlled trials

The aim of this study is to compare the efficacy of different exercise interventions for adult flexible flatfoot. Nine databases (PubMed, EMBASE, Web of Science, the Cochrane Central Register of Controlled Trials (CENTRAL), SCOPUS, PRDro, Google Scholar, China National Knowledge Infrastructure(CNKI) and Wanfang data) were systematically searched from their inception until February 2024. The search resulted in 2112 records, with 11 studies included. All networks revealed low heterogeneity and non-significant inconsistency (I2 ≤ 25.0%). Three network plots were formed for navicular drop. Firstly, compared with the control group, strengthening the posterior tibial muscle + stretching the iliopsoas muscle + TCE (MD: 3.32, 95% CI: 1.78, 4.89), PNF (MD: 1.81, 95%CI: -0.05, 3.70), SFE (MD: 1.23, 95%CI: 1.02, 1.44) all showed better effects. And strengthening the posterior tibial muscle + stretching the iliopsoas muscle + TCE exercise is considered to be the most effective intervention, with SUCRA of 0.97. Secondly, compared with the control group, hip-focused neuromuscular exercise (MD: 6.22, 95% CI: -1.69, 14.12), SFE with EMG biofeedback (MD: -0.81, 95%CI: -1.59, 3.21) all showed better effects. And hip-focused neuromuscular exercise is considered to be the most effective intervention, with SUCRA of 0.92. Thirdly, the internal foot muscle training combined with gluteus muscle strengthening is significantly better than the other two groups, with SUCRA of 0.99. For the foot posture index, comprehensive reinforcement (MD: 1.95, 95% CI − 0.19, 4.03) showed better effects compared with the control group. In the probability ranking table, comprehensive reinforcement is significantly better than the other two groups, with SUCRA of 0.98. For the foot function index, orthoses wear + stretching + eccentric progressive resistive exercise of tibialis posterior is significantly better than the other two groups, with SUCRA of 0.92. In conclusion, various exercise therapies improve the arch shape and function of patients with flexible flatfoot, particularly hip muscle and lower limb overall muscle training.

Efficacy and safety of Vonoprazan-based treatment of Helicobacter pylori infection: a systematic review and network meta-analysis

ObjectiveThe aim of this study was to evaluate the effectiveness and safety of the nine most widely studied Vonoprazan (VPZ)-based treatment regimens along with traditional Proton pump inhibitor (PPI)-based treatment regimens in eradicating Helicobacter pylori (H. pylori) infection.DesignThrough searching PubMed, Embase, Cochrane Library, Web of Science, we exclusively included randomized controlled trials (RCTs) to investigate the efficacy of VPZ-based and PPI-based therapies for H. pylori infection. The included studies were evaluated for methodological quality using the Cochrane bias risk assessment tool, and the data analysis software was used to analyze the data accordingly.ResultsThe RCTs were collected from the earliest available date up to August 2023. Twenty-one RCTs were included, with a total sample size of 5481. The results of the network meta-analysis showed that the eradication rate of the VPZ-based quadruple 14-day (VPZ-Q14) treatment regimen in Intention-to-treat (ITT) analysis was the highest (SUCRA: 0.874); The eradication rate of the VPZ-based quadruple 10-day (VPZ-Q10) treatment plan in Per-protocol (PP) analysis was the highest (SUCRA: 0.849). All regimens were well tolerated without significant differences. According to the probability ranking of safety, high-dose VPZ-based dual 14-day therapy (H-VPZ-D14) ranked first in SUCRA, reaching 0.952. This indicates that H-VPZ-D14 treatment is the safest with a relatively low incidence of adverse effect. Therefore, VPZ-based therapies not only have a higher eradication rate, but also possess satisfactory safety.ConclusionCompared with traditional PPI-based therapies, VPZ-based therapies have shown superior eradication effects. Based on the Ranking Plot of the Network, the VPZ-Q14 or VPZ-Q10 treatment regimen for H. pylori has a higher eradication rate and acceptable differences compared to other treatment regimens. In addition, for regions with high antibiotic resistance rates, we recommend a 14-day quadruple therapy with bismuth based on VPZ.

Current interventional model for movement in Parkinson's disease: network meta-analysis based on the improvement of motor ability.

To identify optimally therapeutic exercise interventions for improving motor ability among patients with Parkinson's disease (PD), we conducted a network meta-analysis (NMA) of randomized controlled trials comparing different exercise regimens. Relevant RCTs were retrieved by searching PubMed, Embase, Cochrane, Web of Science, CINAHL, CBM, China National Knowledge Infrastructure (CNKI), Wan fang, VIP, and other databases from inception to July 9, 2023 is available in English as the primary language. Exercise outcomes as measured by Movement Disorder Society- Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS-III) score change were evaluated and ranked using STATA software version 18.0. All included studies were assessed for methodological quality using the Cochrane Risk of Bias tool. The final NMA included 71 studies involving 3,732 participants, 87 intervention experiments, and 27distinct interventions. Although most exercise interventions showed some efficacy (reducing MDS-UPDRS-III score), cumulative ranking probability surface (SUCRA) values indicated that the best exercise interventions for motor function improvement were archery (95.6%), riding a bicycle (80.9%), and binary rhythm dance (80.8%). An exercise intervention comprising archery, cycling, and(or) binary rhythm dance may yield superior improvements in motor function among patients with Parkinson's disease.