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We show that a two-sided matching platform can successfully compete by limiting the number of choices it offers to its customers, while charging higher prices than platforms with unrestricted choice. We develop a stylized model of online dating where agents with different outside options match based on how much they like each other. Starting from these microfoundations, we derive the strength and direction of indirect network effects and show that increasing the number of potential matches has a positive effect due to larger choice, but also a negative effect due to competition between agents on the same side. Agents resolve the trade-off between these competing effects differently, depending on their outside options. For agents with high outside options, the choice effect is stronger than the competition effect, leading them to prefer an unrestricted-choice platform. The opposite is the case for agents with low outside options, who then have higher willingness to pay for a platform restricting choice, as it also restricts the choice set of their potential matches. Moreover, since only agents with low outside options self-select into the restricted choice platform, the competition effect is mitigated further. This allows multiple platforms offering different number of choices to coexist without the market tipping. This paper was accepted by Bruno Cassiman, business strategy.

This paper considers a class of generalized methods of moments (GMM) estimators for general dynamic panel models, allowing for weakly exogenous covariates and cross-sectional dependence due to spatial lags, unspecified common shocks, and time-varying interactive effects. We significantly expand the scope of the existing literature by allowing for endogenous time-varying spatial weight matrices without imposing explicit structural assumptions on how the weights are formed. An important area of application is in social interaction and network models where our specification can accommodate data dependent network formation. We consider an exemplary social interaction model and show how identification of the interaction parameters is achieved through a combination of linear and quadratic moment conditions. For the general setup we develop an orthogonal forward differencing transformation to aid in the estimation of factor components while maintaining orthogonality of moment conditions. This is an important ingredient to a tractable asymptotic distribution of our estimators. In general, the asymptotic distribution of our estimators is found to be mixed normal due to random norming. However, the asymptotic distribution of our test statistics is still chi-square.

CBR is a crucial metric used to assess the durability of base course materials and subgrade soils in various types of pavements. In this research, the machine learning (ML) approach has been implemented using random forest (RF), decision tree (DT), linear regression (LR), and artificial neural network (ANN) models to estimate CBR (California bearing ratio) values of the soil based on seven predictors such as maximum dry density, soil classification, optimum moisture content, liquid limit, plastic limit, plastic index, and swell, which can be easily determined from the laboratory. AASHTO M 145 was used to categorize 252 soil samples that formed the basis of an experimental data set. In this model study, the data were split into 20% test data and 80% training data. Standard statistical measures including coefficient of determination, correlations, and errors were used to assess the effectiveness of the models such as MSE (mean squared error), MAE (mean absolute error), and RMSE (root mean square error). From these evaluation metrics, the random forest algorithm gets a smaller error and larger relative error (R2) value to compare with other algorithms. Therefore, it can be concluded that a random forest algorithm based on the analysis findings can accurately forecast the soil’s CBR.

Of particular interest within fifth generation (5G) cellular networks are the typical levels of radiofrequency (RF) electromagnetic fields (EMFs) emitted by ‘small cells’, low-power base stations, which are installed such that both workers and members of the general public can come in close proximity with them. In this study, RF-EMF measurements were performed near two 5G New Radio (NR) base stations, one with an Advanced Antenna System (AAS) capable of beamforming and the other a traditional microcell. At various positions near the base stations, with distances ranging between 0.5 m and 100 m, both the worst-case and time-averaged field levels under maximized downlink traffic load were assessed. Moreover, from these measurements, estimates were made of the typical exposures for various cases involving users and non-users. Comparison to the maximum permissible exposure limits issued by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) resulted in maximum exposure ratios of 0.15 (occupational, at 0.5 m) and 0.68 (general public, at 1.3 m). The exposure of non-users was potentially much lower, depending on the activity of other users serviced by the base station and its beamforming capabilities: 5 to 30 times lower in the case of an AAS base station compared to barely lower to 30 times lower for a traditional antenna.

In the current work, a systematic approach is exercised to monitor amended soil reliability for a housing development program to holistically understand the targeted material mixture and the building input derived, focusing on the three governing parameters: (i) optimum moisture content (OMC), (ii) maximum dry density (MDD), and (iii) unconfined compressive strength (UCS). It is in essence the selection of machine learning algorithms that could optimally show the true relation of these factors in the best possible way. Thus, among the machine learning approaches, the optimizable ensemble and artificial neural networks were focused on. The data sources were those compiled from wide-ranging literature sources distributed over the five continents and twelve countries of origin. After a rigorous manipulation, synthesis, and results analyses, it was found that the selected algorithms performed well to better approximate OMC and UCS, whereas that of the MDD result falls short of the established threshold of the set limits referring to the MSE statistical performance evaluation metrics.

Understanding the dynamic behavior of soil is crucial for developing effective mitigation strategies for natural hazards such as earthquakes, landslides, and soil liquefaction, which can cause significant damage and loss of life. The ultrasonic wave testing method provides a non-invasive and reliable way of measuring the shear modulus, damping ratio and density of soils, which are fundamental parameters for understanding soil’s dynamic characteristics. The aim of this study was to investigate the effects of environmental factors, such as water salinity, soil liquid limit, plasticity index, dry density, and water content, on ultrasonic wave velocities (specifically shear and primary waves) in kaolin–sand mixtures subjected to near-zero vertical stress, as well as to predict these effects utilizing two unique artificial intelligence methods, including Classification and Regression Random Forests (CRRF) and Artificial Neural Networks (ANN), which, to our knowledge, have not been utilized in previous literature. The CRRF and ANN models were developed using two well-known algorithms and five different architectures using a database of 128 datasets. Water salinity, dry density, water content, liquid limit and plasticity index were predictor variables. The results showed that both CRRF and ANN were highly accurate. The coefficient of determination (R 2 ) and mean absolute error (MAE) of the best CRRF were 0.963 and 9.191, respectively to predict V s , and 0.974 and 7.809 to predict V p , respectively. Furthermore, in ANN, R 2 and MAE were respectively 0.994 and 0.016 to predict both V s and V p . According to importance analysis, liquid limit, molality, and dry density are the most critical parameters, while water content is the least critical.

Due to instrument sensitivities and algorithm detection limits, level 2 (L2) Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) 532 nm aerosol extinction profile retrievals are often populated with retrieval fill values (RFVs), which indicate the absence of detectable levels of aerosol within the profile. In this study, using 4 years (2007–2008 and 2010–2011) of CALIOP version 3 L2 aerosol data, the occurrence frequency of daytime CALIOP profiles containing all RFVs (all-RFV profiles) is studied. In the CALIOP data products, the aerosol optical thickness (AOT) of any all-RFV profile is reported as being zero, which may introduce a bias in CALIOP-based AOT climatologies. For this study, we derive revised estimates of AOT for all-RFV profiles using collocated Moderate Resolution Imaging Spectroradiometer (MODIS) Dark Target (DT) and, where available, AErosol RObotic NEtwork (AERONET) data. Globally, all-RFV profiles comprise roughly 71 % of all daytime CALIOP L2 aerosol profiles (i.e., including completely attenuated profiles), accounting for nearly half (45 %) of all daytime cloud-free L2 aerosol profiles. The mean collocated MODIS DT (AERONET) 550 nm AOT is found to be near 0.06 (0.08) for CALIOP all-RFV profiles. We further estimate a global mean aerosol extinction profile, a so-called “noise floor”, for CALIOP all-RFV profiles. The global mean CALIOP AOT is then recomputed by replacing RFV values with the derived noise-floor values for both all-RFV and non-all-RFV profiles. This process yields an improvement in the agreement of CALIOP and MODIS over-ocean AOT.

California bearing ratio (CBR) is an indispensable parameter in the design of road pavement, repeated carrying out of this test has been chiefly monotonous and time wasting, also the use of cement as stabilizer has also been increasingly expensive, hence, the need for admixing with agrowaste ash such as rice husk ash (RHA). This research is carried out for the prediction of the CBR of lateritic soil admixed with cement and RHA by means of an artificial neural network (ANN). Six parameters are selected as input variables to obtain results that are accurate and precise. The six input variables are cement, RHA, liquid limit, plasticity index, maximum dry density and optimum moisture content, while CBR Unsoaked and CBR Soaked are the output variables. The study consists of a database of 1288 samples obtained from laboratory experiments which were subdivided into 70% for training, 15% for testing, and 15% for validation. The training operation is performed by a multilayer perceptron-back propagation algorithm. The network topology is achieved after fixing a number of hidden neurons. Thereafter, statistical indices are used in evaluating the performance of the ANN model. It is established that this model is appropriate for accurate prediction of CBR results.

This study aims to assess performances of eleven Machine Learning (ML) methods in predicting the Groundwater Quality Index (GWQI) for Yazd, an arid province in Iran. The ML models encompass Multiple Linear Regression (MLR), Support Vector Regression (SVR), K-Nearest Neighbors, Decision Tree Regression, Adaptive Boosting or AdaBoost, Random Forest Regression, Gradient Boosting Regression (GBR), XGBoost Regression (XGBR), Gaussian Process (GP), Artificial Neural Network (ANN), and Multi-Gene Genetic Programming (MGGP). After optimizing ML hyperparameters, ML-based estimation models were developed for three scenarios depending on which water quality parameters were used as input data: (1) K + and pH; (2) K + , pH, Na + , Ca 2+ , SO 4 2- , HCO 3 - and Mg 2+ ; and (3) K + , pH, Na + , Ca 2+ , SO 4 2- , HCO 3 - , Mg 2+ , Cl - , EC, TH, and TDS. For each scenario, ML-based models were assessed further by conducting (i) reliability analysis, (ii) ranking analysis, and (iii) confidence limits check. The results of the first scenario (with two input data) demonstrated the superiority of ANN, MGGP and GP, whereas ANN, MGGP and GBR were the most robust for the second scenario (with seven input data). Furthermore, the ranking analysis indicated that MLR, GP and ANN achieved the first highest ranks when eleven water quality parameters (third scenario) were used. The reliability analysis revealed that GP, MGGP, MLR, ANN, GBR, and XGBR achieved the highest reliability percentages across different scenarios, with ANN consistently ranking among the top models. Finally, the comprehensive comparative analysis of ML performances in this study reveals their potential for predicting GWQI. Highlights • Groundwater quality was assessed using a dataset collected from wells of an arid region • Eleven machine learning models were evaluated for estimating groundwater quality index • Three scenarios were compared based on WHO permissible limits • Reliability and ranking analyses were conducted for estimations of each ML model

Nonlinear friction in high-precision, ultra-low-speed servo systems severely degrades performance, causing low-speed crawling, static errors, and limit-cycle oscillations. This study introduces the LuGre friction model to describe these phenomena mathematically and proposes an improved genetic algorithm (GA) for precise parameter identification. Simulations demonstrate that LuGre-based feedforward compensation outperforms conventional proportional-integral-derivative (PID) control, effectively mitigating speed tracking errors and enhancing both speed and position accuracy. Experimental validation on a linear motor platform confirms the method’s efficacy, achieving a 25.1% improvement in tracking accuracy. The results highlight the practical relevance of this approach for precision servo systems. This work has achieved a practical identification framework for LuGre parameters, combining GA optimization with transient/steady-state data, feedforward compensation that directly injects estimated friction forces, bypassing feedback delays and experimental verification of the method’s industrial applicability.