Random Errors Research Articles

Understanding error rejection of differential impedance spectroscopy for the in situ characterization of highly conductive fluids

Abstract Differential impedance spectroscopy (DIS) can offer distinct advantages over conventional impedance spectroscopy (IS), particularly for the in situ characterization of highly conductive, corrosive fluids. To enhance the understanding of DIS and determine the conditions under which this method is preferable over conventional IS with fixed electrode positions, error calculations were performed. Two scenarios were analyzed, one accounting solely for random instrument errors and the other including random errors as well as systematic errors from a serial offset impedance. Our analyses reveal that while permittivity measurements of highly conductive media remain challenging regardless of whether a conventional or differential impedance approach is used, DIS enables highly accurate conductivity measurements across a wide frequency and conductivity range. Studying different cell dimensions with equal cell constants revealed that despite the increased influence of systematic errors at small scales, conductivity can still be accurately measured, underscoring the applicability of DIS for microfluidic applications. Other potential parasitic effects, such as those arising from the electromagnetic skin effect, stray capacitances, electrode size, and temperature gradients, are discussed, providing a comprehensive framework for sample characterization with DIS.

An improved multiple-outlier-robust GNSS/INS EKF filer based on multiple statistical similarity measure

Abstract High-precision and highly reliable location information is the key support for autonomous driving applications. Global Navigation satellite system (GNSS)/Inertial navigation system (INS) integrated positioning is a typical high-precision positioning technology. However, satellite signals are easily affected by various interferences such as signal blocking and multipath problems, leading to performance degradation in complex urban environments. The traditional extended Kalman filtering (EKF) cannot handle observation outlier, and the system’s positioning accuracy sharply declines when the observations are affected by non-Gaussian heavy-tailed noise. Therefore, this paper presents an improved multiple-outlier-robust extended Kalman filter (I-MOREKF) for GNSS/INS Loosely Coupled Integration. A multiple statistical similarity measure (MSSM) is built to evaluate the similarity between two random vectors from dimension to dimension. Then, the I-MOREKF is proposed by maximizing a cost function based on the MSSM, and considering the robust estimation with IGG III model. The proposed model effectively removes multiple types of errors in complex environments. A vehicular test was carried out to validate the feasibility and performance of the proposed model. Simulated random gross errors are introduced in GNSS measurements, and the measurement noises are corrupted by heavy-tail noise. The results show that simulated gross errors can be successfully detected with I-MOREKF model, and the 3D position Root Meas Square Error is 0.0148 m for I-MOREKF, which are improved by 97.2%,13.5% and 52.9%, as compared with EKF, IGG Ⅲ and MOREKF.

Microbiome-based correction for random errors in nutrient profiles derived from self-reported dietary assessments.

Since dietary intake is challenging to directly measure in large-scale cohort studies, we often rely on self-reported instruments (e.g., food frequency questionnaires, 24-hour recalls, and diet records) developed in nutritional epidemiology. Those self-reported instruments are prone to measurement errors, which can lead to inaccuracies in the calculation of nutrient profiles. Currently, few computational methods exist to address this problem. In the present study, we introduce a deep-learning approach --- M icrobiom e -based nu t rient p r of i le c orrector (METRIC), which leverages gut microbial compositions to correct random errors in self-reported dietary assessments using 24-hour recalls or diet records. We demonstrate the excellent performance of METRIC in minimizing the simulated random errors, particularly for nutrients metabolized by gut bacteria in both synthetic and three real-world datasets. Further research is warranted to examine the utility of METRIC to correct actual measurement errors in self-reported dietary assessment instruments.

A comparison of conventional and resampled personal reliability in detecting careless responding.

Detecting careless responding in survey data is important to ensure the credibility of study findings. Of several available detection methods, personal reliability (PR) is one of the best-performing indices. Curran, Journal of Experimental Social Psychology, 66, 4-19, (2016) proposed a resampled version of personal reliability (RPR). Compared to the conventional PR or even-odd consistency, in which just one set of scale halves is used, RPR is based on repeated calculation of PR across several randomly rearranged sets of scale halves. RPR should therefore be less affected than PR by random errors that may occur when a specific set of scale half pairings is used for the PR calculation. In theory, RPR should outperform PR, but it remains unclear whether it in fact does, and under what conditions the potential gain in detection accuracy is the most pronounced. We conducted two studies: a simulation study examined the performance of the conventional PR and RPR in detecting simulated careless responding, and a real data example study analyzed their performance when detecting human-generated careless responding. In both studies, RPR turned out to be a significantly better careless response indicator than PR. The results also revealed that using 25 resamples for the RPR computation is sufficient to obtain the expected gain in detection accuracy over the conventional PR. We therefore recommend using RPR instead of the conventional PR when screening questionnaire data for careless responding.

Enhanced slit methodology for characterization of x‐ray focal properties

AbstractThe measurement of focal spot sizes in industrial x‐ray tubes using conventional edge and slit methods typically requires complex slit diaphragms and auxiliary testing equipment. Additionally, stringent spatial‐positioning requirements are imposed on the x‐ray tube, auxiliary testing equipment, and detector, making the machining of auxiliary testing equipment and meeting test conditions challenging. This study optimized and improved the auxiliary testing equipment and computations in the slit method based on the principles of the edge method. An experimental setup capable of self‐calibrating the spatial positions was designed, avoiding measurement errors during focal‐spot‐size measurements. We derived a formula for the focal spot size for this experimental setup, allowing measurements using slits of various sizes. Furthermore, we proposed a method for determining the focal spot size through linear fitting of multiple sets of slit dimensions and projected widths of the focal spot on the detector. This approach filters out random noise and errors during data collection, significantly enhancing measurement precision. By adjusting the slit and detector angles, we could measure the focal spot sizes at various angles and draw contour maps of the focal spot shape. This method was applied to measure the focal spot size and shape of a side‐window x‐ray tube under different voltage conditions. The overall measurement error was less than 6%, reflecting the reliability, accuracy, and consistency of the results obtained using the proposed method. Thus, the proposed method supports the inspection, verification, and maintenance of x‐ray sources.

Data transformation and model selection in bivariate allometry.

Students of biological allometry have used the logarithmic transformation for over a century to linearize bivariate distributions that are curvilinear on the arithmetic scale. When the distribution is linear, the equation for a straight line fitted to the distribution can be back-transformed to form a two-parameter power function for describing the original observations. However, many of the data in contemporary studies of allometry fail to meet the requirement for log-linearity, thereby precluding the use of the aforementioned protocol. Even when data are linear in logarithmic form, the two-parameter power equation estimated by back-transformation may yield a misleading or erroneous perception of pattern in the original distribution. A better approach to bivariate allometry would be to forego transformation altogether and to fit multiple models to untransformed observations by nonlinear regression, thereby creating a pool of candidate models with different functional form and different assumptions regarding random error. The best model in the pool of candidate models could then be identified by a selection procedure based on maximum likelihood. Two examples are presented to illustrate the power and versatility of newer methods for studying allometric variation. It always is better to examine the original data when it is possible to do so.

CT perfusion parameter estimation in stroke using neural network with transformer and physical model priors

ObjectivesCT perfusion (CTP) imaging is vital in treating acute ischemic stroke by identifying salvageable tissue and the infarcted core. CTP images allow quantitative estimation of CT perfusion parameters, which can provide information on the degree of tissue hypoperfusion and its salvage potential. Traditional methods for estimating perfusion parameters, such as singular value decomposition (SVD) and its variations, are known to be sensitive to noise and inaccuracies in the arterial input function. To our knowledge, there has been no implementation of deep learning methods for CT perfusion parameter estimation. Materials & methodsIn this work, we propose a deep learning method based on the Transformer model, named CTPerformer-Net, for CT perfusion parameter estimation. In addition, our method incorporates some physical priors. We integrate physical consistency prior, smoothness prior and the physical model prior through the design of the loss function. We also generate a simulation dataset based on physical model prior for training the network model. ResultsIn the simulation dataset, CTPerformer-Net exhibits a 23.4 % increase in correlation coefficients, a 95.2 % decrease in system error, and a 90.7 % reduction in random error when contrasted with block-circulant SVD. CTPerformer-Net successfully identifies hypoperfused and infarcted lesions in 103 real CTP images from the ISLES 2018 challenge dataset. It achieves a mean dice score of 0.36 for the infarct core segmentation, which is slightly higher than the commercially available software (dice coefficient: 0.34) used as a reference level by the challenge. ConclusionExperimental results on the simulation dataset demonstrate that CTPerformer-Net achieves better performance compared to block-circulant SVD. The real-world patient dataset confirms the validity of CTPerformer-Net.

Deformation-Adapted Spatial Domain Filtering Algorithm for UAV Mining Subsidence Monitoring

Underground coal mining induces surface subsidence, leading to disasters such as damage to buildings and infrastructure, landslides, and surface water accumulation. Preventing and controlling disasters in subsidence areas and reutilizing land depend on understanding subsidence regularity and obtaining surface subsidence monitoring data. These data are crucial for the reutilization of regional land resources and disaster prevention and control. Subsidence hazards are also a key constraint to mine development. Recently, with the rapid advancement of UAV technology, the use of UAV photogrammetry for surface subsidence monitoring has become a significant trend in this field. The periodic imagery data quickly acquired by UAV are used to construct DEM through point cloud filtering. Then, surface subsidence information is obtained by differencing DEM from different periods. However, due to the accuracy limitations inherent in UAV photogrammetry, the subsidence data obtained through this method are characterized by errors, making it challenging to achieve high-precision ground surface subsidence monitoring. Therefore, this paper proposes a spatial domain filtering algorithm for UAV photogrammetry combined with surface deformation caused by coal mining based on the surface subsidence induced by coal mining and combined with the characteristics of the surface change. This algorithm significantly reduces random error in the differential DEM, achieving high-precision ground subsidence monitoring using UAV. Simulation and field test results show that the surface subsidence elevation errors obtained in the simulation tests are reduced by more than 50% compared to conventional methods. In field tests, this method reduced surface subsidence elevation errors by 39%. The monitoring error for surface subsidence was as low as 8 mm compared to leveling survey data. This method offers a new technical pathway for high-precision surface subsidence monitoring in mining areas using UAV photogrammetry.

An enhanced stochastic error modeling using multi-Gauss–Markov processes for GNSS/INS integration system

Angular random walk (ARW), rate random walk (RRW), and bias instability (BI) are the main noise types in inertial measurement units (IMUs) and thus determine the navigation performance of IMUs. BI is the flicker noise, which determines the noise level of an inertial sensor. The traditional error modeling approach involves modeling the ARW and BI processes as RRW or Gauss–Markov (GM) processes, and this approach is applied as a suboptimal filter in the global navigation satellite system (GNSS)/inertial navigation system (INS) extended Kalman filter (EKF). In this paper, the random error identification processes for white noise and colored noise for inertial sensors are separated using the Allan variance and power spectral density methods and the equivalence of the stochastic process differential equations of bias instability and a combination of multiple first-order GM processes are derived. A colored noise compensation method is proposed based on the enhanced EKF model. Experimental results demonstrate that, compared to traditional error models, our proposed model reduces positional drift error in dynamic testing from 195 to 49 m, enhancing positional accuracy by 40.2%. These findings confirm the potential and superiority of our method in complex navigation environments.

Effects of acupuncture on ischemic stroke: A systematic review with meta-analyses and trial sequential analyses

BackgroundAlthough acupuncture is widely used to treat ischemic stroke, its effects remain uncertain. This systematic review aims to synthesize current evidence on the effects of acupuncture for ischemic stroke and assess whether current randomized controlled trials (RCTs) have sufficient power to detect its effects. MethodsSeven databases and two registry platforms were searched systematically from inception to June 13, 2023, to identify RCTs comparing the effects of acupuncture on ischemic stroke with control groups (placebo/blank). The Cochrane Risk of Bias 2 (RoB 2) tool was used to evaluate the risk of bias in the included trials. Random effects models through restricted maximum likelihood estimation were further used to estimate the pooled mean differences (MDs) and the corresponding 95 % confidence intervals (CIs). The primary outcome was neurological function (National Institutes of Health Stroke Scale, NIHSS), while secondary outcomes included global disability (modified Rankin Scale, mRS) and activities of daily living (ADLs) (Barthel Index, BI or Modified Barthel Index, MBI). The Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) system was used to evaluate the certainty of the evidence. Trial sequential analysis (TSA) was further applied to control random errors and calculate the required information size (RIS). ResultsForty RCTs involving 4485 patients were included. Acupuncture was found to significantly improve NIHSS (18 trials, 2658 patients; MD = −1.61, 95 % CI [-2.12, −1.09], low certainty evidence), mRS (3 trials, 298 patients; MD = −0.34, 95 % CI [-0.50, −0.19], moderate certainty evidence), and BI/MBI (26 trials, 2562 patients; MD = 8.98, 95 % CI [6.18, 11.77], low certainty evidence). Further, graphs of TSA indicated that the sample size of the trials was sufficient, and the results are robust. ConclusionCurrent evidence suggests that acupuncture can significantly improve neurological function, global disability, and ADLs in patients with ischemic stroke. The results were robust, as confirmed by TSA. However, the certainty of the evidence is moderate to low and should be further verified by more high-quality RCTs.

Characterizing the uncertainty of CMORPH products for estimating orographic precipitation over Northern California

Satellite-based precipitation products (SPPs), such as the NOAA Climate Prediction Center (CPC) Morphing technique (CMORPH), have greatly extended our ability to monitor global precipitation. However, their performance over complex terrain remains highly uncertain. To improve accuracy, CPC has recently upgraded its operational SPP to the second generation CMORPH–CMORPH2. In addition to such efforts, the reliability of SPPs can be further enhanced by developing robust uncertainty estimates. This study employs a censored, shifted gamma distribution (CSGD)-based error modeling framework to develop error models for both CMORPH2 and its predecessor, CMORPH1, at a 6-hourly scale over Northern California—a typical complex terrain region that challenges most SPPs. Using the Stage IV reference precipitation, the relative improvements of CMORPH2 over CMORPH1 are quantified by comparing their CSGD-derived error statistics. The comparison shows that CMORPH2 outperforms CMORPH1 by reducing the overall bias and detection errors, and by better capturing the orographic gradients of precipitation over the Coast Ranges and Sierra Nevada. Validation results show that the trained error models can appropriately represent the bias and random errors; however, the models for both CMORPH2 and CMORPH1 show reduced skills in the high-altitude Sierra Nevada. With high-resolution regional climate simulations, the uncertainty estimates are further improved by incorporating them as covariates. The simulated precipitation shows substantial improvement of the uncertainty estimates over most areas, including the challenging high-altitude Sierra Nevada. Following precipitation, the simulated integrated water vapor transport (IVT) and convective available potential energy (CAPE) also offer modest improvements, mainly along the coast. Independent verification further demonstrates the robustness of the uncertainty estimates during heavy precipitation events. As NOAA is currently reprocessing CMORPH2 to produce an updated global precipitation record from 1991 onward, this error modeling framework holds potential for broader applications in quantifying the uncertainty of CMORPH2 for estimating orographic precipitation over extended periods and locations.

Random measurement and prediction errors limit the practical relevance of two velocity sensors to estimate the 1RM back squat.

While maximum strength diagnostics are applied in several sports and rehabilitative settings, dynamic strength capacity has been determined via the one-repetition maximum (1RM) testing for decades. Because the literature concerned several limitations, such as injury risk and limited practical applicability in large populations (e.g., athletic training groups), the strength prediction via the velocity profile has received increasing attention recently. Referring to relative reliability coefficients and inappropriate interpretation of agreement statistics, several previous recommendations neglected systematic and random measurement bias. This article explored the random measurement error arising from repeated testing (repeatability) and the agreement between two common sensors (vMaxPro and TENDO) within one repetition, using minimal velocity thresholds as well as the velocity = 0m/s method. Furthermore, agreement analyses were applied to the estimated and measured 1RM in 25 young elite male soccer athletes. The results reported repeatability values with an intraclass correlation coefficient (ICC) = 0.66-0.80, which was accompanied by mean absolute (percentage) errors (MAE and MAPE) of up to 0.04-0.22m/s and ≤7.5%. Agreement between the two sensors within one repetition showed a systematic lower velocity for the vMaxPro device than the Tendo, with ICCs ranging from 0.28 to 0.88, which were accompanied by an MAE/MAPE of ≤0.13m/s (11%). Almost all estimations systematically over/ underestimated the measured 1RM, with a random scattering between 4.12% and 71.6%, depending on the velocity threshold used. In agreement with most actual reviews, the presented results call for caution when using velocity profiles to estimate strength. Further approaches must be explored to minimize especially the random scattering.

Measuring Data Quality from Building Registers: A Case Study in Italy

Geographic data quality is a complex issue requiring continuous operational improvements. Considering this to be one of the most topical research and technical issues in official statistics and environmental monitoring, this study re-connects the operational dimension of ‘geographic data quality’ with the broader issue of monitoring the quality of official statistics. By estimating the accuracy of public (spatially explicit) data, this study illustrates an operational framework with an exploratory exercise in estimating the geographic data quality characteristic of a specific information source within the official statistical system (i.e., building registry) in a given European country, namely, Italy. The results of this exercise provide a paradigmatic example of profound innovation in the activities of statistical services in Europe and specifically the Italian National Statistical Institute (Istat), transitioning from independent (and poorly connected) field surveys to an integrated system of registries. Since several studies are based on spatially explicit survey units, it is essential to estimate the quality of geographical data, especially those derived from information sources where space is topical information, such as (local, regional, or national) building registers. Thanks to the results of an empirical exercise applied to Italian building registers, the present article will discuss the issue of data accuracy, considered the main issue related to monitoring geographic data quality, from an official statistics’ perspective. Statistical indicators will be proposed for the assessment of systematic and random errors of spatially explicit measures, possibly enabling a quali-quantitative improvement in the semantic content of building registers that address the inherent requirements of official statistics. Such indicators have some positive implications for the entire system of official statistics in Italy and, for generalization, within the European Statistical System.

Parameter estimation methods for correlated observation mixed additive and multiplicative random error model

In the domain of geodetic adjustment, current methodologies addressing additive and multiplicative error models operate under the assumption of independence between additive and multiplicative random errors. However, limited research has been conducted on elucidating the correlation between the components of additive and multiplicative random errors. To extend a methodology of mixed error models with additive and multiplicative correlation observations, this paper derives three parameter adjustment methods: correlation observation least squares, correlation observation weighted least squares, and correlation observation bias-corrected weighted least squares, all based on the principles of least squares. Additionally, corresponding unit weight variance estimations are constructed. Finally, it is verified by two numerical simulation experiments and a real-world application example of a geodetic network that the correlated observation bcWLS studied in this paper proves to be the most efficient among the six methods for solving the correlated observation multiplication mixture error model.

“Oh Grandmother, what big teeth you have!” Incentives to spur scientific research at business schools have been treacherous

Scientific research was introduced in business schools in the mid-1950s to raise the quality of business education as well as to improve actual business practices. Schools expected scientific research to provide a benign and respected path toward improvements, and government support for research and students’ strong interest in studying business brought financial resources and hiring opportunities that made large changes possible. Consequently, hundreds of business schools began urging professors to publish scientific research. Sixty years later, plentiful evidence is showing that scientific research has not brought expected benefits for business operations; on the contrary, business-school research has generated a high percentage of untrustworthy “findings”. This disappointing outcome has six interacting causes: (1) Business schools have expanded by hiring professors who study fundamental topics of economics, psychology, and sociology rather than topics relevant for business practices, so few research findings have been adopted and tested by firms. (2) Instead of evaluating the quality or consequences of research, schools have relied on evaluations by journal editors and reviewers, whose overriding criteria emphasize theory at the expense of practical implications. (3) Editorial evaluations have high error rates because editors and reviewers only know what researchers choose to tell them, and because the complexity of manuscripts causes editors and reviewers to disagree frequently. (4) Schools have incentivized professors by making promotions and job security depend on publishing numerous papers in “prestigious” journals. (5) Many professors have responded to these incentives by engaging in covert practices that make studies unlikely to be supported in replications, and some professors have gone so far as to manufacture fake data. (6) Researchers, editors, and reviewers have largely relied on null-hypothesis statistical significance tests, which are prone to interpret small variations or random errors in data as “significant” findings. Although there are fresh efforts to make research findings more reliable and impactful, the need for further interventions to reform the present knowledge-production system is all too apparent.

Analysis of intra-fractional positioning correction performed by cone beam computed tomography in SBRT treatments

PurposeThis study aims to evaluate the positioning correction extracted from Intra-fraction Cone Beam (IF-CBCT) images obtained during Stereotactic Body Radiotherapy (SBRT) treatments, and to assess whether its magnitude justifies its acquisition. In addition, the results obtained in lung, liver, and pancreas SBRTs with two deep inspiration breath-hold systems (DIBH), and for prostate with/without ultrasound (US) monitoring were compared. Methods1449 treatments, performed with two linear accelerators (LINACs) were retrospectively analyzed. DIBH were performed either with a spirometry-based device or a surface-guidance system and one LINAC was equipped with US monitoring system for prostate. Significance tests were used to account for differences between units. ResultsGroup systematic error (M) was approximately –0.7 mm for DIBH treatments in superior-inferior (SI) direction with no difference (p > 0.7) between LINACs. Moreover, there was a SI difference of 0.5 mm for prostate treatments (p = 0.008), in favor of the US monitored one. In anterior-posterior (AP) direction, only liver treatments exhibited differences between LINACs, with the spirometer-based system being 0.8 mm inferior (p = 0.003). M<0.4 mm in left–right (LR) direction was found for all locations and LINACs. The spirometer-based system resulted in lower standard deviation of systematic and random errors in most components and locations, with a greater effect observed in liver SBRTs. ConclusionsThe corrections made with IF-CBCT during SBRT treatments were not negligible. Both DIBH systems were effective in managing respiratory movements. However, the spirometry-based system was slightly more accurate. In addition, US monitoring of the prostate appeared to be useful in reducing target shift.

An estimation method for bias error of measurements by utilizing process data, an incidence matrix and a reference instrument for data validation and reconciliation: Part 2 Extension to the energy balance relation

Abstract Ensuring data reliability is becoming increasingly important for further applications of AI, IoT, and digital twins. One promising technology for ensuring data reliability is data validation and reconciliation (DVR), which minimizes the uncertainty of measurements based on statistics. DVR has been widely used for the operation and maintenance of nuclear power plants in Europe and the United States in recent years. The most important input for DVR analysis is measurement uncertainty. The catalog value provided by sensor manufacturers includes the measurement uncertainty, but in reality, the actual measurement uncertainty is often smaller. Previous studies have proposed several methods for evaluating the actual measurement uncertainty based on process data, which have been confirmed to be effective for evaluating random errors. It is important to note that bias errors also contribute significantly to the measurement uncertainty. In our previous paper, we proposed a method for estimating bias error using process data, an incidence matrix, and a reference instrument. The proposed method was limited to a mass balance relation, i.e., flow rate measurements. In this paper, we extend the method to include an energy balance relation by considering energy conservation in addition to mass conservation. This extension enables the evaluation of measurement uncertainty for flow rate and temperature. The proposed method was validated with two benchmark problems. It was found to be applicable to various flow conditions, including physically fluctuating flow, such as that observed in the feedwater flow in nuclear power plants.

Efficacy of antidepressants in functional dyspepsia: Systematic review and meta-analysis with trial sequential analysis of randomized controlled trials.

The beneficial effects of using antidepressants in improving functional dyspepsia (FD) symptoms have been reported in previous meta-analyses; however, the results have not been conclusive. The aim was to perform an updated meta-analysis coupled with trial sequential analysis (TSA) to assess the efficacy of the use of any antidepressants in the treatment of FD in adults. Electronic databases were searched up to March 2024 for randomized controlled trials (RCTs) recruiting adults with FD. Data of overall symptoms improved between the antidepressants and placebo groups was pooled to obtain risk ratio (RR) employing the random-effects model. The effect of random errors was evaluated with TSA. The Grading of Recommendations Assessment, Development and Evaluation (GRADE)approach was used to assess the certainty of evidence. Analyses were performed using STATA version 16.0. Nine RCTs with 924 patients met the eligible criteria. The RRs of FD symptoms improving with any antidepressants, tricyclic antidepressants (TCAs) and selective serotonin reuptake inhibitors were (n = 9, RR = 1.30 [95% CI, 1.02-1.67]), (n = 5, RR = 1.41 [95% CI, 1.07-1.85]) and (n = 2, RR = 0.97 [95% CI, 0.72-1.29]),respectively. TSA demonstrated conclusive evidence for the beneficial effect of TCAs. The number needed to treat (NNT) with any depressants and TCAs were 11 (95% CI, 7-36) and 6 (95% CI, 4-15), respectively. The certainty of the evidence for an effect of TCAs was that of moderate GRADE quality. The benefit, however, was limited to the western population (n = 3, RR = 1.43 [95% CI, 1.04-1.96]) and did not extend to the Asian population (n = 2, RR = 1.32 [95% CI, 0.75-2.32]). Conversely, antidepressant-using patients experienced adverse events more frequently. However, no statistically significant association was found between TCAs and any adverse events (n = 3; RR = 1.36 [95% CI, 0.91-2.04]). Evidence was obtained suggesting TCAs can be an effective alternative in the treatment of FD, but more evidence from high-quality large trials is required to support their use, especially in the Asian population.

Trends analysis of Evapotranspiration and responses to soil moisture and wind speed over the Taklimakan Desert, China

The Tarim Basin is a typical hyper-arid area, 57% of which is occupied by the Taklimakan desert. Because evapotranspiration (ET) is a key factor in determining water demand, accurate estimation of ET is critical for efficient water management in arid regions. In this study, the spatiotemporal dynamics of ET across the Tarim Basin from 2001 to 2020 were simulated based on a revised Surface Energy Balance System (SEBS) algorithm, and the impacts of driving factors on ET trends in the Taklimakan desert were quantified. The findings indicated that the mean annual ET in the entire basin was slightly greater than that in the desert (302 mm vs. 295 mm), although the decreasing rate of annual ET across the entire basin was slower than that in the desert (−0.27 mm/year vs. −0.50 mm/year). Strong increasing trends were found in croplands, whereas decreasing trends, which accounted for 56.5% of the basin, were primarily concentrated in the central and southern parts of the desert. Correlation analysis revealed that wind speed (WS) and soil moisture (SM) were the dominant factors, with a negative contribution of more than 35% and a positive contribution of more than 45%, respectively, to ET trends. Compared with the ERA5-Land and complementary relationship ET using the extended triple collocation method, SEBS ET provided detailed information and a lower random error standard deviation. Our findings contribute to the understanding of how dominant factors impact ET in hyper-arid regions and provide a reference for water management.

The Effects of Age and Treatment Intensity on Behavioral Target Mastery With Applied Behavior Analysis (ABA) Intervention Using Causal Moderation Models.

Introduction Applied behavior analysis (ABA) is a therapy that focuses on improving specific behaviors using positive and negative reinforcement through antecedents, behaviors, and consequences, particularly in individuals with autism and other developmental disorders. It uses the principles of learning theory to bring about meaningful and positive changes in behavior. In ABA treatment, intensityrefers to the amount and frequency of therapy an individual receives. This includes weekly hours, session trials, and overall duration. Intensive treatment involves more hours and trials tailored to individual needs and responses. Younger individuals, particularly those with autism, often receive more intensive therapy because early intervention leads to better outcomes. Programs may recommend 25-40 hours per week for young children. As children age, therapy may become less intensive, focusing on specific skills. The study explores how age and treatment intensity affect the mastery of behavioral targets in ABA interventions. Materials and methods This study involved 100 participants (89 children, four adults, and seven instances where the individuals' ages were not recorded due to random data entry errors (MCAR)) who received ABA treatment over three months. The treatments included functional analysis, discrete trials, and massand naturalistic training. Data on the mastery of target behaviors were collected using the Catalystsoftware (New York, New York). The primary outcome was the percentage of mastered behavioral targets, indicating the effectiveness of the ABA treatment. Several predictors were examined, including the participant's age and treatment intensity variables, such as the average number of trials and teaching days to achieve behavioral mastery. The interaction effects between age and these treatment intensity variables were analyzed. The study used descriptive and inferential statistics to explore these interactions, including correlational and multiple regression analyses with causal moderator modeling. Results In Model 1, a baseline multiple regression analysis showed that average teaching days significantly predict the percentage of targets mastered. However, its limited explanatory power suggests other variables also play a role. Model 2 introduced interaction effects using causal models, revealing that age moderates the relationship between treatment variables and behavioral outcomes. This model provided a more nuanced understanding but still had room for improvement. Model 3 further refined the approach, achieving higher R-values and lower standard error. It highlighted age's significant role in modifying the impact of teaching days on mastery. This model's superior performance emphasizes the importance of considering age as a moderating factor in ABA interventions, leading to more effective and personalized behavior therapy. Conclusions This study significantly enhances our understanding of the complex interactions between age and treatment intensity within ABAinterventions. Practitioners and researchers can develop more tailored and effective therapeutic strategies by identifying and leveraging these interactions. This approach optimizes the treatment process and ensures that interventions are personalized to meet the unique needs of each individual. Ultimately, this leads to more successful outcomes in behavioral therapy, fostering improved adaptive behaviors and overall development.