A QoS-Based Fairness-Aware BBR Congestion Control Algorithm Using QUIC

Congestion control is a fundamental technology to balance the traffic load and the network. The Internet Engineering Task Force (IETF) Quick UDP Internet Connection (QUIC) protocol has flexible congestion control and at the same time possesses the advantages of high efficiency, low latency, and easy deployment at the application layer. Bottleneck bandwidth and round-trip propagation time (BBR) is an optional congestion control algorithm adopted by QUIC. BBR can significantly increase throughput and reduce latency, in particular over long-haul paths. However, BBR results in high packet loss in low bandwidth and low fairness in multi-stream scenarios. In this article, we propose the enhanced BBR congestion control (eBCC) algorithm, which improves the BBR algorithm in two aspects: (1) 10.87% higher throughput and 74.58% lower packet loss rate in the low-bandwidth scenario and (2) 8.39% higher fairness in the multi-stream scenario. This improvement makes eBCC very suitable for IoT communications to provide better QoS services

Compressive Strength Prediction of High-Strength Concrete Using Long Short-Term Memory and Machine Learning Algorithms

Compressive strength is an important mechanical property of high-strength concrete (HSC), but testing methods are usually uneconomical, time-consuming, and labor-intensive. To this end, in this paper, a long short-term memory (LSTM) model was proposed to predict the HSC compressive strength using 324 data sets with five input independent variables, namely water, cement, fine aggregate, coarse aggregate, and superplasticizer. The prediction results were compared with those of the conventional support vector regression (SVR) model using four metrics, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient (R2). The results showed that the prediction accuracy and reliability of LSTM were higher with R2 = 0.997, RMSE = 0.508, MAE = 0.08, and MAPE = 0.653 compared to the evaluation metrics R2 = 0.973, RMSE = 1.595, MAE = 0.312, MAPE = 2.469 of the SVR model. The LSTM model is recommended for the pre-estimation of HSC compressive strength under a given mix ratio before the laboratory compression test. Additionally, the Shapley additive explanations (SHAP)-based approach was performed to analyze the relative importance and contribution of the input variables to the output compressive strength

Compressive Strength Prediction of High-Strength Concrete Using Long Short-Term Memory and Machine Learning Algorithms

Compressive strength is an important mechanical property of high-strength concrete (HSC), but testing methods are usually uneconomical, time-consuming, and labor-intensive. To this end, in this paper, a long short-term memory (LSTM) model was proposed to predict the HSC compressive strength using 324 data sets with five input independent variables, namely water, cement, fine aggregate, coarse aggregate, and superplasticizer. The prediction results were compared with those of the conventional support vector regression (SVR) model using four metrics, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient (R2). The results showed that the prediction accuracy and reliability of LSTM were higher with R2 = 0.997, RMSE = 0.508, MAE = 0.08, and MAPE = 0.653 compared to the evaluation metrics R2 = 0.973, RMSE = 1.595, MAE = 0.312, MAPE = 2.469 of the SVR model. The LSTM model is recommended for the pre-estimation of HSC compressive strength under a given mix ratio before the laboratory compression test. Additionally, the Shapley additive explanations (SHAP)-based approach was performed to analyze the relative importance and contribution of the input variables to the output compressive strength

Abnormal Thalamocortical Circuit in Adolescents With Early-Onset Schizophrenia

Objective: Thalamic circuit imbalance characterized by increased sensorimotor−thalamic connectivity and decreased prefrontal−thalamic connectivity has been consistently observed in adult-onset schizophrenia (AOS), although it is unclear whether this pattern is also a feature of early-onset schizophrenia (EOS). If this is the case, thalamic circuit imbalance can be considered as a core mechanistic defect in schizophrenia, unconfounded by the age of onset. Method: A total of 116 adolescents with EOS (63 drug-naive EOS) and 55 matched healthy controls (HC) were recruited and underwent resting-state functional magnetic resonance imaging scans. To define the specific location of the thalamic subregions in thalamocortical circuit, 16 atlas-based thalamic subdivisions were used in functional connectivity analysis. Results: The EOS group showed increased sensorimotor−thalamic connectivity and decreased prefrontal-cerebello−thalamic connectivity, consistent with AOS. Sensorimotor−thalamic hyperconnectivity was more prominent than prefrontal−thalamic hypoconnectivity, which was circumscribed to the medial prefrontal cortex (mPFC), in EOS. Of note, the EOS group specifically exhibited strengthened thalamic connectivity with the salience network (SN). In addition, the EOS showed a more prominent disruption of the lateral thalamic nuclear connectivity. Conclusion: Thalamic dysconnectivity observed in the EOS extends the observations from adult patients. Sensorimotor−thalamic hyperconnectivity is critical for the expression of schizophrenia phenotype irrespective of the age of onset, raising the possibility of aberrant but accelerated functional network maturation in EOS. The specific thalamocortical dysconnectivity involving the SN and mPFC may underlie the distinctive features of multi-modal hallucinations and heightened emotional valence of psychosis seen in EOS