Interfacial characteristics between bitumen and corrosion products on steel slag surface from molecular scale

Corrosion commonly happened on the surface of steel slag during the weathering and accumulation process, whose products would form weak points and affect the interface between bitumen and steel slag. To clear its characteristics in the atomic scales, the interface between bitumen and corrosion products was investigated by molecular dynamics (MD) simulations. Firstly, bitumen model, corrosion products model and bitumen-corrosion products systems were constructed. Different simulated temperatures were applied on the systems to reach equilibrium with NVT (constant number of atoms, volume, and temperature) ensemble. The interaction effect in the interface were evaluated by geometric adsorption index, interaction energy, adhesion work and surface free energy. Diffusion coefficient and relative concentration were used to evaluate the diffusion and aggregation. Finally, the pull-out test was conducted on the equilibrium models to determine the debonding behaviors at the interface. The results show that the interaction effect in Bitumen-FeO system was the strongest while that in Bitumen-FeOOH system was the weakest, which can be proved by surface free energy and debonding behaviors. The temperature changing would affect van der Waals energy but had no obvious association with coulombic energy. The adhesion between bitumen and corrosion products was contributed by non-bond interaction energy which consisted of van der Waals interaction for Fe3O4, Fe2O3 and FeOOH, and van der Waals and electrostatic interaction for FeO. The most severe aggregation of bitumen occurred in Bitumen-FeO system, which was more likely caused by electrostatic interaction. Furthermore, the change of velocity and thickness led to the failure transformation from cohesion to adhesion. The strong interaction in Bitumen-FeO system increase the possibility of cohesion failure in the debonding process

Deep learning driven diagnosis of malignant soft tissue tumors based on dual-modal ultrasound images and clinical indexes

BackgroundSoft tissue tumors (STTs) are benign or malignant superficial neoplasms arising from soft tissues throughout the body with versatile pathological types. Although Ultrasonography (US) is one of the most common imaging tools to diagnose malignant STTs, it still has several drawbacks in STT diagnosis that need improving.ObjectivesThe study aims to establish this deep learning (DL) driven Artificial intelligence (AI) system for predicting malignant STTs based on US images and clinical indexes of the patients.MethodsWe retrospectively enrolled 271 malignant and 462 benign masses to build the AI system using 5-fold validation. A prospective dataset of 44 malignant masses and 101 benign masses was used to validate the accuracy of system. A multi-data fusion convolutional neural network, named ultrasound clinical soft tissue tumor net (UC-STTNet), was developed to combine gray scale and color Doppler US images and clinic features for malignant STTs diagnosis. Six radiologists (R1-R6) with three experience levels were invited for reader study.ResultsThe AI system achieved an area under receiver operating curve (AUC) value of 0.89 in the retrospective dataset. The diagnostic performance of the AI system was higher than that of one of the senior radiologists (AUC of AI vs R2: 0.89 vs. 0.84, p=0.022) and all of the intermediate and junior radiologists (AUC of AI vs R3, R4, R5, R6: 0.89 vs 0.75, 0.81, 0.80, 0.63; p <0.01). The AI system also achieved an AUC of 0.85 in the prospective dataset. With the assistance of the system, the diagnostic performances and inter-observer agreement of the radiologists was improved (AUC of R3, R5, R6: 0.75 to 0.83, 0.80 to 0.85, 0.63 to 0.69; p<0.01).ConclusionThe AI system could be a useful tool in diagnosing malignant STTs, and could also help radiologists improve diagnostic performance

Prediction model of obstructive sleep apnea–related hypertension: Machine learning–based development and interpretation study

BackgroundObstructive sleep apnea (OSA) is a globally prevalent disease closely associated with hypertension. To date, no predictive model for OSA-related hypertension has been established. We aimed to use machine learning (ML) to construct a model to analyze risk factors and predict OSA-related hypertension.Materials and methodsWe retrospectively collected the clinical data of OSA patients diagnosed by polysomnography from October 2019 to December 2021 and randomly divided them into training and validation sets. A total of 1,493 OSA patients with 27 variables were included. Independent risk factors for the risk of OSA-related hypertension were screened by the multifactorial logistic regression models. Six ML algorithms, including the logistic regression (LR), the gradient boosting machine (GBM), the extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bootstrapped aggregating (Bagging), and the multilayer perceptron (MLP), were used to develop the model on the training set. The validation set was used to tune the model hyperparameters to determine the final prediction model. We compared the accuracy and discrimination of the models to identify the best machine learning algorithm for predicting OSA-related hypertension. In addition, a web-based tool was developed to promote its clinical application. We used permutation importance and Shapley additive explanations (SHAP) to determine the importance of the selected features and interpret the ML models.ResultsA total of 18 variables were selected for the models. The GBM model achieved the most extraordinary discriminatory ability (area under the receiver operating characteristic curve = 0.873, accuracy = 0.885, sensitivity = 0.713), and on the basis of this model, an online tool was built to help clinicians optimize OSA-related hypertension patient diagnosis. Finally, age, family history of hypertension, minimum arterial oxygen saturation, body mass index, and percentage of time of SaO2 < 90% were revealed by the SHAP method as the top five critical variables contributing to the diagnosis of OSA-related hypertension.ConclusionWe established a risk prediction model for OSA-related hypertension patients using the ML method and demonstrated that among the six ML models, the gradient boosting machine model performs best. This prediction model could help to identify high-risk OSA-related hypertension patients, provide early and individualized diagnoses and treatment plans, protect patients from the serious consequences of OSA-related hypertension, and minimize the burden on society

Design och verifiering av en energieffektiv Edge-Pursuit-jämförare

With the rapid development of mobile communication, sensors, and biomedical in recent years, the demand for accurate data information, highquality audio and image has become much more significant, which requires a high-precision Analog to Digital Converter (ADC) to process weak analog signals. As one of the core modules of ADC, the comparator’s precision, speed, stability, and noise play a key role in the performance of the whole circuit. Over the years, those performance has been improved a lot by both designing new architectures and using advanced fabrication technology. However, the conventional comparators occupy 50%-60% of the total energy consumption of EPC, even with advanced technology and lower supply voltage. In this thesis, a new type of energy-efficient comparator, called Edge-Pursuit Comparator (EPC), is proposed, which satisfies the need for low comparison energy. The design of EPC is based on a ring oscillator, when the EPC enters the evaluation mode, two signal edges with different propagation delays will chase in it until one overlaps the other, and finally generate a stable voltage level in each output node. The circuit is built and simulated in Cadence Virtuoso using cmos22fdsoi technology. The simulation results reveal that the energy consumed per comparison is dependent on the input differential voltage, and it can be as low as 7 fJ when vin = 50 mV, which is around ten times smaller compared with conventional comparators. In addition, as the power consumption is considerable when the two input voltages are very close, a promising improvement is applied to EPC, namely connecting every node with a variable capacitor, which is called Edge-Pursuit Comparator enhanced with Capacitor (EPCC). Cadence simulation results prove that EPCC can largely lower the energy consumption under a small vin while keeping input-referred noise the same. Therefore, a combination of EPC and EPCC is expected to have prospective applications in the energy-efficient area.Med den snabba utvecklingen av mobil kommunikation, sensorer och biomedicin under de senaste åren har efterfrågan på korrekt datainformation, högkvalitativt ljud och bild blivit mycket mer betydande, vilket kräver en högprecision Analog till Digital Converter (ADC) för att bearbeta svaga analoga signaler. Som en av ADC:s kärnmoduler spelar komparatorns precision, hastighet, stabilitet och brus en nyckelroll i prestanda för hela kretsen. Under årens lopp har dessa prestanda förbättrats mycket genom att både designa nya arkitekturer och använda avancerad tillverkningsteknik. De konventionella komparatorerna upptar dock 50%-60% av den totala energiförbrukningen för EPC, även med avancerad teknik och lägre matningsspänning. I detta examensarbete föreslås en ny typ av energieffektiv komparator, kallad Edge-Pursuit Comparator (EPC), som tillgodoser behovet av låg jämförelseenergi. Designen av EPC är baserad på en ringoscillator, när EPC:n går in i utvärderingsläget kommer två signalkanter med olika utbredningsfördröjningar att jaga i den tills den ena överlappar den andra, och slutligen generera en stabil spänningsnivå i varje utgångsnod. Kretsen är byggd och simulerad i Cadence Virtuoso med hjälp av cmos22fdsoiteknik. Simuleringsresultaten visar att energiförbrukningen per jämförelse är beroende av ingångsdifferensspänningen och den kan vara så låg som 7 fJ när vin = 50 mV, vilket är cirka tio gånger mindre jämfört med konventionella komparatorer. Dessutom, eftersom strömförbrukningen är avsevärd när de två inspänningarna är mycket nära, tillämpas en lovande förbättring på EPC, nämligen att ansluta varje nod med en variabel kondensator, som kallas Edge-Pursuit Comparator förbättrad med kondensator (EPCC). Kadenssimuleringsresultat bevisar att EPCC till stor del kan sänka energiförbrukningen under en liten vin samtidigt som ingångsreferat buller hålls detsamma. Därför förväntas en kombination av EPC och EPCC ha potentiella tillämpningar inom det energieffektiva området

Design och verifiering av en energieffektiv Edge-Pursuit-jämförare

With the rapid development of mobile communication, sensors, and biomedical in recent years, the demand for accurate data information, highquality audio and image has become much more significant, which requires a high-precision Analog to Digital Converter (ADC) to process weak analog signals. As one of the core modules of ADC, the comparator’s precision, speed, stability, and noise play a key role in the performance of the whole circuit. Over the years, those performance has been improved a lot by both designing new architectures and using advanced fabrication technology. However, the conventional comparators occupy 50%-60% of the total energy consumption of EPC, even with advanced technology and lower supply voltage. In this thesis, a new type of energy-efficient comparator, called Edge-Pursuit Comparator (EPC), is proposed, which satisfies the need for low comparison energy. The design of EPC is based on a ring oscillator, when the EPC enters the evaluation mode, two signal edges with different propagation delays will chase in it until one overlaps the other, and finally generate a stable voltage level in each output node. The circuit is built and simulated in Cadence Virtuoso using cmos22fdsoi technology. The simulation results reveal that the energy consumed per comparison is dependent on the input differential voltage, and it can be as low as 7 fJ when vin = 50 mV, which is around ten times smaller compared with conventional comparators. In addition, as the power consumption is considerable when the two input voltages are very close, a promising improvement is applied to EPC, namely connecting every node with a variable capacitor, which is called Edge-Pursuit Comparator enhanced with Capacitor (EPCC). Cadence simulation results prove that EPCC can largely lower the energy consumption under a small vin while keeping input-referred noise the same. Therefore, a combination of EPC and EPCC is expected to have prospective applications in the energy-efficient area.Med den snabba utvecklingen av mobil kommunikation, sensorer och biomedicin under de senaste åren har efterfrågan på korrekt datainformation, högkvalitativt ljud och bild blivit mycket mer betydande, vilket kräver en högprecision Analog till Digital Converter (ADC) för att bearbeta svaga analoga signaler. Som en av ADC:s kärnmoduler spelar komparatorns precision, hastighet, stabilitet och brus en nyckelroll i prestanda för hela kretsen. Under årens lopp har dessa prestanda förbättrats mycket genom att både designa nya arkitekturer och använda avancerad tillverkningsteknik. De konventionella komparatorerna upptar dock 50%-60% av den totala energiförbrukningen för EPC, även med avancerad teknik och lägre matningsspänning. I detta examensarbete föreslås en ny typ av energieffektiv komparator, kallad Edge-Pursuit Comparator (EPC), som tillgodoser behovet av låg jämförelseenergi. Designen av EPC är baserad på en ringoscillator, när EPC:n går in i utvärderingsläget kommer två signalkanter med olika utbredningsfördröjningar att jaga i den tills den ena överlappar den andra, och slutligen generera en stabil spänningsnivå i varje utgångsnod. Kretsen är byggd och simulerad i Cadence Virtuoso med hjälp av cmos22fdsoiteknik. Simuleringsresultaten visar att energiförbrukningen per jämförelse är beroende av ingångsdifferensspänningen och den kan vara så låg som 7 fJ när vin = 50 mV, vilket är cirka tio gånger mindre jämfört med konventionella komparatorer. Dessutom, eftersom strömförbrukningen är avsevärd när de två inspänningarna är mycket nära, tillämpas en lovande förbättring på EPC, nämligen att ansluta varje nod med en variabel kondensator, som kallas Edge-Pursuit Comparator förbättrad med kondensator (EPCC). Kadenssimuleringsresultat bevisar att EPCC till stor del kan sänka energiförbrukningen under en liten vin samtidigt som ingångsreferat buller hålls detsamma. Därför förväntas en kombination av EPC och EPCC ha potentiella tillämpningar inom det energieffektiva området

A Thermodynamics-Oriented and Neural Network-Based Hybrid Model for Military Turbofan Engines

Traditional thermodynamic models for military turbofans suffer from non-convergence and inaccuracy due to inaccuracy of the component maps and the instability of the iterative process. To address these problems, a thermodynamically oriented and neural network-based hybrid model for military turbofans is proposed. Different from iteration-based thermodynamic models, the proposed hybrid model transforms the iteration process into a multi-objective optimization and training process for a component-level neural network in order to improve convergence and modeling accuracy. The experiment shows that the accuracy of the proposed hybrid model can reach about 7%, 5% better than the map-fitting-based thermodynamic model and 8% better than the purely data-driven method, with a similar number of network neutrons, verifying its effectiveness. The contributions of this work mainly lie in the following aspects: a new component-level neural network structure is proposed to improve convergence and computational efficiency; a multi-objective loss function based on component co-working is proposed to direct the model to converge toward the physical thermodynamic process; a fusion training method of multiple data sources is established to train the model with good convergence and high computational accuracy

Optimizing Energy Efficiency in Metro Systems Under Uncertainty Disturbances Using Reinforcement Learning

In the realm of urban transportation, metro systems serve as crucial and sustainable means of public transit. However, their substantial energy consumption poses a challenge to the goal of sustainability. Disturbances such as delays and passenger flow changes can further exacerbate this issue by negatively affecting energy efficiency in metro systems. To tackle this problem, we propose a policy-based reinforcement learning approach that reschedules the metro timetable and optimizes energy efficiency in metro systems under disturbances by adjusting the dwell time and cruise speed of trains. Our experiments conducted in a simulation environment demonstrate the superiority of our method over baseline methods, achieving a traction energy consumption reduction of up to 10.9% and an increase in regenerative braking energy utilization of up to 47.9%. This study provides an effective solution to the energy-saving problem of urban rail transit.Comment: 11 page

Prediction of Hydrolysis Pathways and Kinetics for Antibiotics under Environmental pH Conditions: A Quantum Chemical Study on Cephradine

Understanding hydrolysis pathways and kinetics of many antibiotics that have multiple hydrolyzable functional groups is important for their fate assessment. However, experimental determination of hydrolysis encounters difficulties due to time and cost restraint. We employed the density functional theory and transition state theory to predict the hydrolysis pathways and kinetics of cephradine, a model of cephalosporin with two hydrolyzable groups, two ionization states, two isomers and two nucleophilic attack directions. Results showed that the hydrolysis of cephradine at pH = 8.0 proceeds via opening of the β-lactam ring followed by intramolecular amidation. The predicted rate constants at different pH conditions are of the same order of magnitude as the experimental values, and the predicted products are confirmed by experiment. This study identified a catalytic role of the carboxyl group in the hydrolysis, and implies that the carboxyl group also plays a catalytic role in the hydrolysis of other cephalosporin and penicillin antibiotics. This is a first attempt to quantum chemically predict hydrolysis of an antibiotic with complex pathways, and indicates that to predict hydrolysis products under the environmental pH conditions, the variation of the rate constants for different pathways with pH should be evaluated