Output Bias Research Articles

The Nature of Technological Change in the Chinese Hotel Sector

This article investigates the role of technological change in the Chinese hotel sector over the period 2005 to 2015. The analysis is conducted at the provincial level and on the star-rating hotel basis. A three-step approach is adopted. First, the Malmquist productivity index and its decomposition into efficiency and technological change is estimated. Second, the significance of each component is statistically tested. Third, the technological change is decomposed to analyze the Hicks neutrality assumption. The findings show that the Chinese hotel sector experienced an overall productivity growth, which is mainly and significantly driven by a technological progress. In most of the cases, the technological change is not neutral, and input and output biases are characterized.

Validity Concerns of Using Machine Learning in Management Research

The adoption of machine learning (ML) techniques in business research has significantly grown in recent years, with many studies using off-the-shelf ML algorithms to replace the labor-intensive coding of large unstructured datasets. Most published studies, however, failed to examine how errors in ML outputs affect the validity of the study results. This article shows, if unchecked, even a very small machine learning error (e.g., 5% for a binary output) could be amplified in subsequent statistical analysis to produce significant changes in research outcome, hence threatening its validity. After clearly differentiating two seemingly interchangeable concepts (i.e., ML error in the ML output vs. ML-induced bias in the empirical-analysis output), we develop a theoretical framework that demonstrates the possibility of having a large ML-induced bias despite of a small ML error. We discuss three common sources of the error-to-bias amplification stemming from the design of an ML algorithm. To mitigate the amplification, we introduce a novel correction process that not only significantly outperforms baseline approaches commonly adopted in the existing literature, but also can be readily adopted for a wide variety of ML algorithms and empirical studies. These advantages are demonstrated through a study that applies a state-of-the-art ML algorithm for facial recognition using real-world data.

Blue Energy Collection toward All‐Hours Self‐Powered Chemical Energy Conversion

AbstractThe conversion and transmission of blue energy in the ocean are critical issues. By employing triboelectric nanogenerators (TENGs), blue energy can be harvested but the corresponding electricity transmission and storage are still great challenges. In this work, an automatic high‐efficiency self‐powered energy collection and conversion system is proposed that converts blue energy to chemical energy. A gear‐driven unidirectional acceleration TENG is designed to convert disordered and low‐frequency water wave energy to low voltage and high current DC output. The output bias from the TENG can be used to drive a Ti–Fe2O3/FeNiOOH based photoelectrochemical cell under sunlight to produce hydrogen. Moreover, under the situation without sunlight, the self‐powered system can be automatically switched to another working state to charge a Co3O4 based lithium‐ion battery. The hydrogen production rate reaches to 4.65 µL min‐1 under sunlight at the rotation speed of 120 rpm. The conversion efficiency of the whole system is calculated to be 2.29%. The system triggered by photoswitches can automatically switch between two working states with or without sunlight and convert the blue energy to either hydrogen energy or battery energy for easy storage and transmission, which widens the future applications for blue energy.

WRF‐based dynamical downscaling of ERA5 reanalysis data for High Mountain Asia: Towards a new version of the High Asia Refined analysis

AbstractThe High Asia Refined analysis (HAR) is a regional atmospheric data set generated by dynamical downscaling of the Final operational global analysis (FNL) using the Weather Research and Forecasting (WRF) model. It has been successfully and widely utilized. A new version (HAR v2) with longer temporal coverage and extended domains is currently under development. ERA5 reanalysis data is used as forcing data. This study aims to find the optimal set‐up for the production of the HAR v2 to provide similar or even better accuracy as the HAR. First, we conducted a sensitivity study, in which different cumulus, microphysics, planetary boundary layer, and land surface model schemes were compared and validated against in situ observations. The technique for order preference by similarity to the ideal solution (TOPSIS) method was applied to identify the best schemes. Snow depth in ERA5 is overestimated in High Mountain Asia (HMA) and causes a cold bias in the WRF output. Therefore, we used Japanese 55‐year Reanalysis (JRA‐55) to correct snow depth initialized from ERA5 based on the linear scaling approach. After applying the best schemes identified by the TOPSIS method and correcting the initial snow depth, the model performance improves. Finally, we applied the improved set‐up for the HAR v2 and computed a one‐year run for 2011. Compared to the HAR, the HAR v2 has a better representation of air temperature at 2 m. It produces slightly higher precipitation amounts, but the spatial distribution of seasonal mean precipitation is closer to observations.

Effect of Pharmacologic Prophylaxis on Venous Thromboembolism After Radical Prostatectomy: The PREVENTER Randomized Clinical Trial

BackgroundDirect high-quality evidence is lacking evaluating perioperative pharmacologic prophylaxis (PP) after radical prostatectomy (RP) to prevent venous thromboembolism (VTE) leading to significant practice variation. ObjectiveTo study the impact of in-hospital PP on symptomatic VTE incidence and adverse events after RP at 30 d, with the secondary objective of evaluating overall VTE in a screening subcohort. Design, setting, and participantsA prospective, phase 4, single-center, randomized trial of men with prostate cancer undergoing open or robotic-assisted laparoscopic RP was conducted (July 2017–November 2018). InterventionPP (subcutaneous heparin) plus routine care versus routine care alone. The screening subcohort was offered lower extremity duplex ultrasound at 30 d. Outcomes measurements and statistical analysisThe primary efficacy outcome was symptomatic VTE incidence (pulmonary embolism [PE] or deep venous thrombosis [DVT]). Primary safety outcomes included the incidence of symptomatic lymphocele, hematoma, or bleeding after surgery. Secondary outcomes were overall VTE, estimated blood loss, total surgical drain output, complications, and surveillance imaging bias. Fisher’s exact test and modified Poisson regression were performed. Results and limitationsA total of 501 patients (75% robotic) were randomized and >99% (500/501) completed follow-up. At second interim analysis (N = 445), the symptomatic VTE rate was 2.3% (four PE + DVT and one DVT) for routine care versus 0.9% (one PE + DVT and one DVT) for PP (relative risk 0.40 [95% confidence interval 0.08–2.03], p = 0.3) meeting a futility threshold for early stopping. In the screening subcohort, the overall VTE rate was 3.3% versus 2.4% (p = 0.7). Results were similar at the final analysis (symptomatic VTE: 2.0% vs 0.8%, p = 0.3; overall VTE: 2.9% vs 2.8%, p = 1). No differences were observed in safety or secondary outcomes. All VTE events (seven symptomatic and three asymptomatic) occurred in patients undergoing pelvic lymph node dissection. ConclusionsThis study was not able to demonstrate a statistically significant reduction in symptomatic VTE associated with PP. There was no increase in the development of symptomatic lymphoceles, bleeding, or other adverse events. Given that the event rate was lower than powered for, further research is needed among high-risk patients (Caprini score ≥8) or patients receiving pelvic lymph node dissection. Patient summaryIn this report, we randomized patients undergoing radical prostatectomy to perioperative pharmacologic prophylaxis or routine care alone. We found that pharmacologic prophylaxis did not reduce postoperative symptomatic venous thromboembolism significantly for men at routine risk. Importantly, pharmacologic prophylaxis did not increase adverse events, such as formation of lymphoceles or bleeding, and can safely be implemented when indicated for patients with risk factors undergoing radical prostatectomy.

A multi-parametric simulation study of Neural Networks’ Performance for non-linear data against linear regression analysis in Economics

Different mathematical and dynamic methods have been developed addressing the problem of forecasting, with the regression analysis to be one of the most frequently used statistical procedures. Meanwhile, neural networks (NNs) are considered to be well suited in finding accurate solutions in an environment characterised by volatility, noisy, irrelevant or partial information. In this chapter, a simulation study compares the performance of NNs against linear regression analysis is based on multiple combinations (421 in total) of five different factors providing those cases that the NN performs better than the LRM and defining the output bias as the main contributor to the NN outcome.

Predicting Student Study Failure Risk Using Artificial Neural Network Method

Background: Study failure in a course is very complicated which is always affected by many different factors and is characterized by uncertainties. We reviewed previous literatures and patents relating to student failure prediction, while there seems to be no ideal method or tool which can easily learn the relationship between failure results and reasons. Methods: In this study a method of artificial neural network was provided to predict student failure risk in course study. A three-layered network topology was used including input layer with nine neural nodes, hidden layer with ten optimized neural nodes, and output layer with one neural node, which is advantageous for dealing with this complicated and uncertain issue. The whole modeling process includes four stages: output inference, loss evaluation, weights and biases training, and model testing. Results: In our sample data there are 577 students in total, including 433 train cases and 144 test cases, and for each sample, there are nine input dependent variables and one output target variable. All calculation and optimization results were implemented based on the TensorFlow and Python. The model accuracy measurements of relative root mean square error on the total, test and train data sets were 0.1637, 0.1596, and 0.1607 respectively, and consistency was shown by testing the predicted results with our observed data, which indicated that the method was promising for predicting student failure risk in course study. Conclusion: It can be used to identify at-risk students with study difficulty and is of practical significance for educators to provide pedagogical supports and interventions in early time to the at-risk students to help them avoid academic failure, and it is also of theoretic significance to improve the whole efficiency of early warning education management.

A global streamflow reanalysis for 1980–2018

Global and continental scale hydrological reanalysis datasets receive growing attention due to their increasing number of applications, ranging from water resources management, climate change studies, water related hazards and policy support. Until recently, their use was mostly limited to qualitative assessments, due to their coarse spatial and temporal resolution, large uncertainty and bias in the model output, and limited extent of the dataset in space and time. This research reports on the setup of a gridded hydrological model with quasi-global coverage, able to reproduce a seamless 39-year streamflow simulation in all world’s medium to large river basins. The model was calibrated at 1226 river sections with a total drainage area of 51 million km2 within 66 countries, using ECMWF’s latest atmospheric reanalysis ERA5. A performance assessment revealed large improvements in reproducing past discharge observations, in comparison to the calibration used in the current operational setup of the hydrological model as part of the Copernicus – Global Flood Awareness System (GloFAS, www.globalfloods.eu), with median scores of Kling-Gupta Efficiency KGE = 0.67 and correlation r = 0.8. The simulation bias was also dramatically reduced and narrowed around zero, with more than 60% of stations showing percent bias within ±20%. Pronounced regional differences in the simulation results remain, pointing out the need for detailed investigation of the hydrological processes in specific regions, including parts of Africa and South Asia. In addition, observed discharges with high data quality is key to achieving skillful model output. The new calibrated model will become part of the operational runs of GloFAS in the next system release foreseen for Spring 2020, together with a near real time extension of the streamflow reanalysis.

The road weather model RoadSurf (v6.60b) driven by the regional climate model HCLIM38: evaluation over Finland

Abstract. In this paper, we evaluate the skill of the road weather model RoadSurf to reproduce present-day road weather conditions in Finland. RoadSurf was driven by meteorological input data from cycle 38 of the high-resolution regional climate model (RCM) HARMONIE-Climate (HCLIM38) with ALARO physics (HCLIM38-ALARO) and ERA-Interim forcing in the lateral boundaries. Simulated road surface temperatures and road surface conditions were compared to observations between 2002 and 2014 at 25 road weather stations located in different parts of Finland. The main characteristics of road weather conditions were accurately captured by RoadSurf in the study area. For example, the model simulated road surface temperatures with a mean monthly bias of −0.3 ∘C and mean absolute error of 0.9 ∘C. The RoadSurf's output bias most probably stemmed from the absence of road maintenance operations in the model, such as snow plowing and salting, and the biases in the input meteorological data. The biases in the input data were most evident in northern parts of Finland, where the regional climate model HCLIM38-ALARO overestimated precipitation and had a warm bias in near-surface air temperatures during the winter season. Moreover, the variability in the biases of air temperature was found to explain on average 57 % of the variability in the biases of road surface temperature. On the other hand, the absence of road maintenance operations in the model might have affected RoadSurf's ability to simulate road surface conditions: the model tended to overestimate icy and snowy road surfaces and underestimate the occurrence of water on the road. However, the overall good performance of RoadSurf implies that this approach can be used to study the impacts of climate change on road weather conditions in Finland by forcing RoadSurf with future climate projections from RCMs, such as HCLIM.

Asymmetry Analysis of the Resonance Curve in Resonant Integrated Optical Gyroscopes.

The Resonant Integrated Optic Gyroscope (RIOG) is a type of high accuracy gyroscope based on the Sagnac effect. A symmetrical resonance curve is very important to the performance of the RIOG. To further investigate and design a RIOG with a waveguide ring resonator, an in-depth research of the asymmetric resonance curve and its influence on the RIOG is fully developed. Four possible optical noises inducing the resonance curve asymmetry are analyzed and their mathematic models are established. These four optical noises are the normal mode effect, the backscattering noise, the backreflection noise and the polarization noise. Any asymmetry of the resonance curve will not only induce a large output bias error into the gyro output, but also seriously decrease the frequency discrimination parameter of the demodulation curve. By using a tunable fiber laser, the high aspect ratio silicon nitride WRR and the silicon dioxide WRR were tested. The experiment measured resonance curves can be well fitted with the theoretical simulation results. The experimental results show that a high aspect ratio silicon nitride waveguide can effectively suppress the polarization noise in the RIOG.

An Armington\u2013Leontief model

We develop a novel linear equilibrium model with an Armington flavor. We provide (1) proof of the solvability of the model and of the existence and non-negativity of the equilibrium solution and of the newly derived multiplier matrix; we also show (2) that the standard Leontief multiplier matrix arises as a special case of this new model and (3) that this model allows the computation of multiplier effects with no external output bias, which is particularly relevant for applied economic analysis.

Cardiac Output Measurement Using the Ultrasonic Cardiac Output Monitor: A Validation Study in Newborn Infants

Objectives: We aimed to determine the accuracy and validity of the Ultrasonic Cardiac Output Monitor (USCOM) measurements of cardiac output (CO) compared to echocardiography in newborn infants, and the inter-rater agreement of USCOM measurements. Methods: In a single-center study we prospectively evaluated neonates undergoing an echocardiographic evaluation. USCOM measurements of CO were obtained at the pulmonary and aortic valve by 2 physicians blinded to the echocardiographic results. All echocardiographic measurements were performed blinded to USCOM measurements. We first enrolled an ascertainment cohort which was subsequently validated in an independent new cohort. Agreement between echocardiography and USCOM methods was assessed by Bland-Altman analysis. Intra-class correlation coefficients (ICC) assessed the agreement between the 2 operators. The ascertainment cohort correction factors were applied in a second validation cohort and agreement of the calibrated measures evaluated with repeat Bland-Altman comparisons. Results: A total of 50 infants were enrolled in the initial cohort and 15 in the validation cohort. There was a high degree of correlation between the USCOM operators (ICC = 0.975). USCOM measurements of CO were significantly higher compared to echocardiography (left ventricular output bias 95 ± 52 mL/kg/min and right ventricular output bias 64 ± 30 mL/kg/min). There was no difference in the subgroup of infants with and without a ductus arteriosus. After the correction was applied to the validation cohort, there was no longer a significant difference between the measures. Conclusions: CO measured by USCOM consistently overestimated the results obtained from echocardiography. USCOM is not adequate to provide absolute estimates of CO. However, it may allow longitudinal hemodynamic assessment of sick neonates.

Modified Integral Control Globally Counters Symmetry-Breaking Biases

We propose a modified integral controller to treat actuation biases in nonholonomic systems. In such systems, although one cannot directly counter biases along all directions, one can still aim at restoring the symmetry of coordinated motion that the biases would break. Focusing on the example of steering controlled planar vehicles, we indeed show how to perfectly stabilize a circular trajectory coordinated with the leader, despite any unknown actuation bias. The proposed solution is a simple modified integral control term, and we prove via a detailed analysis of averaging theory that this control gadget globally stabilizes the perfect coordinated motion. The corresponding general observation is that stabilizing such symmetry-preserving situation can be viewed as rejecting a bias affecting the output measurement, instead of the input command. The symmetry-restoring control then becomes an output bias rejection, dual to the more standard input bias rejection with standard integral control.

Fully-Integrated Charge Pump Design Optimization for Above-Breakdown Biasing of Single-Photon Avalanche Diodes in 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math> </inline-formula>m CMOS

A design methodology for an area-optimized integrated charge pump is described suitable for on-chip high-voltage reverse bias of single-photon avalanche diodes (SPADs). The high-voltage generation block is implemented in a general-purpose 0.13- $\mu \text{m}$ CMOS process and is capable of generating a maximum regulated output voltage of 17.7 V from an input of 1.8 V. An ON–OFF regulation scheme with dynamic charging and discharging capability of the charge pump provides fast recovery of the output bias voltage during SPAD transients, where overshoot and undershoot must both be corrected during active quench and reset. Following a SPAD avalanche current pulse, the measured transient recovery time is 500 ns from a 150-mV overshoot and a 500-mV undershoot to reach 99% of steady-state output. The implemented SPAD bias generation system occupies 0.175-mm2 chip area, without requiring an off-chip load capacitor.

Output and input bias effects of U.S. direct payments

AbstractThis study examines the national effect of U.S. direct payments on the extent and direction of biased technical change on U.S. agriculture. We also assess the economic significance of the estimated bias effects for economic policy modeling endeavors involving a reduction of domestic support payments. A two outputs (livestock and crops) and four inputs (labor, capital, land, and material) translog cost function was estimated from national time series (1948–2011) data. Results indicate that payments do not induce output‐biased technical change. We do find evidence of Hicksian bias that is land using and material input saving attributable to support payments. Global computable general equilibrium simulations suggest that price and output effects of discontinuing direct payments are 1/4 to 1/3 the size once the bias effects are incorporated.

Fin-cooled photovoltaic module modeling – Performances mapping and electric efficiency assessment under real operating conditions

The paper presents the modeling of a fin-cooled photovoltaic (PV) module, under real operating conditions. A full reconstruction of both the thermal behavior – transient energy balance, loss to the environment and module temperature - and the electrical dynamics for the system allows the efficiency gain assessment, associated with an increased heat exchange with ambient air. Various layouts are considered, in terms of fins mass and its effects on the heat capacity, fins amount and fins geometry. The model accounts for the PV module performances dependence on the (i) operating temperature and (ii) solar irradiance and integrates a section for continuous update of I-V PV characteristics, based on a standard five parameters model and the one-diode approximation. The merge between limit performances - as provided by the manufacturer – and evidences from an extensive experimental campaign for the in-field module characterization, allow a detailed reconstruction of the electric power associated with the module operation and prevent any bias in the model output. The potential of fins in module temperature control and electric efficiency enhancement is assessed and confirms the appeal of such a layout with respect to more consolidated cooling techniques.

Development and performance evaluation of new AirGIS – A GIS based air pollution and human exposure modelling system

AirGIS, a Geographic Information Systems (GIS) based air pollution and human exposure modelling system, is routinely used in conjunction with the Operational Street Pollution Model (OSPM®), across the globe, to assess local- or street-scale air pollution. We developed a substantially revised version of AirGIS (hereafter, new AirGIS) as a new modelling system in open-source GIS i.e. PostgreSQL software with its spatial extension PostGIS to (1) optimize the model performance enabling model calculations for a large number of sites over a large geographical area, with limited computing resources (2) replace the outdated programming language Avenue (3) become independent of commercial GIS software. This paper, therefore, aims to describe the overall structure of new AirGIS modelling system together with its strengths and limitations. Furthermore, the new AirGIS has been evaluated against various measured datasets of ambient air pollution (NOx, NO2, PM10 and PM2.5). In terms of reproducing temporal variation (single location, time series of concentrations e.g. annual, daily etc.) of air pollution, the new model achieved correlations (R) in the range 0.45–0.96. While, in terms of reproducing the spatial variation (several locations, single time interval), the new AirGIS achieved correlations in the range 0.32–0.92. The new model, therefore, can be used for both short- and long-term air pollution exposure assessments to facilitate health related studies. However, the present evaluation of the new modelling system also revealed that the new AirGIS significantly overestimated the observed concentrations for two out of four datasets. The possible reasons for these errors and future directions to reducing the bias in the new model output have been discussed.

Search bias quantification: investigating political bias in social media and web search

Users frequently use search systems on the Web as well as online social media to learn about ongoing events and public opinion on personalities. Prior studies have shown that the top-ranked results returned by these search engines can shape user opinion about the topic (e.g., event or person) being searched. In case of polarizing topics like politics, where multiple competing perspectives exist, the political bias in the top search results can play a significant role in shaping public opinion towards (or away from) certain perspectives. Given the considerable impact that search bias can have on the user, we propose a generalizable search bias quantification framework that not only measures the political bias in ranked list output by the search system but also decouples the bias introduced by the different sources—input data and ranking system. We apply our framework to study the political bias in searches related to 2016 US Presidential primaries in Twitter social media search and find that both input data and ranking system matter in determining the final search output bias seen by the users. And finally, we use the framework to compare the relative bias for two popular search systems—Twitter social media search and Google web search—for queries related to politicians and political events. We end by discussing some potential solutions to signal the bias in the search results to make the users more aware of them.

Climate change impacts on water demand and availability using CMIP5 models in the Jaguaribe basin, semi-arid Brazil

The objective of this study was to analyze climate change impacts on irrigation water demand and availability in the Jaguaribe River basin, Brazil. For northeastern Brazil, five global circulation models were selected using a rainfall seasonal evaluation screening technique from the Intergovernmental Panel on Climate Change named Coupled Model Intercomparison Project Phase 5. The climate variables were generated for the base period of 1971–2000, as were projections for the 2025–2055 future time slice. Removal of maximum and minimum temperature and rainfall output bias was used to estimate reference evapotranspiration, irrigation water needs, and river flow using the rainfall—river flow hydrological model Soil Moisture Accounting Procedure for the baseline and future climate (Representative Concentration Pathways 4.5 and 8.5 scenarios). In addition, by applying improved irrigation efficiency, a scenario was evaluated in comparison with field observed performance. The water-deficit index was used as a water availability performance indicator. Future climate projections by all five models resulted in increases in future reference evapotranspiration (2.3–6.3%) and irrigation water needs (2.8–16.7%) for all scenarios. Regarding rainfall projections, both positive (4.8–12.5%) and negative (− 2.3 to − 15.2%) signals were observed. Most models and scenarios project that annual river flow will decrease. Lower future water availability was detected by the less positive water-deficit index. Improved irrigation efficiency is a key measure for the adaptation to higher future levels of water demand, as climate change impacts could be compensated by gains in irrigation efficiency (water demand changes varying from − 1.7 to − 35.2%).

On the Interpretation of 3D Gyroscope Measurements

We demonstrate that the common interpretation of angular velocities measured by a 3D gyroscope as being sequential Euler rotations introduces a systematic error in the sensor orientation calculated during motion tracking. For small rotation angles, this systematic error is relatively small and can be mistakenly attributed to different sources of sensor inaccuracies, including output bias drift, inaccurate sensitivities, and alignments of the sensor sensitivity axes as well as measurement noise. However, even for such small angles, due to accumulation over time, the erroneous rotation interpretation can have a significant negative impact on the accuracy of the computed angular orientation. We confirm our findings using real-case measurements in which the described systematic error just worsens the deleterious effects typically attributed to an inaccurate sensor and random measurement noise. We demonstrate that, in general, significant improvement in the angular orientation accuracy can be achieved if the measured angular velocities are correctly interpreted as simultaneous and not as sequential rotations.