Prediction of Electric Vehicle Energy Consumption in an Intelligent and Connected Environment

Accurate energy consumption prediction is essential for improving the driving experience. In the urban road scenario, we discussed the influencing factors of energy consumption and divided the modes from various perspectives. The differences in energy consumption characteristics and distribution laws for electric vehicles using the IDM and CACC car-following models under different traffic flows are compared. An energy consumption prediction framework based on the LightGBM model is proposed. According to the study, driving range, acceleration, accelerating time, decelerating time and cruising time all significantly impact the overall energy consumption of electric vehicles. There are apparent differences in energy consumption characteristics and distribution laws under different traffic flows: average energy consumption is lower under low flow and increased under high flow. The CACC-electric vehicles consume more energy in low flow than IDM-electric vehicles. Under high flow, the opposite is true. The results show that the proposed framework has a high accuracy: the MAPE based on IDM datasets is 3.45% and the RMSE is 0.039 kWh; the MAPE based on CACC datasets is 5.57% and the RMSE is 0.042 kWh. The MAPE and RMSE are reduced by 33.7% and 50.6% (maximum extent) compared to the best comparison algorithm

Blockchain Smart Contracts for Grid Connection Management in Achieving Net Zero Energy Systems

Energy systems are transitioning towards a decentralized and decarbonized paradigm with the integration of distributed renewable energy sources. Blockchain smart contracts have the increasing potential to facilitate the transition of energy systems due to the natures of automation, standardization, and selfenforcement. This paper proposes a Blockchain smart contracts based platform to manage the grid connection for both large scale generation companies and individual prosumers (both producers and consumers). Through evaluating the capacity margin and carbon intensity for each substation or feeder in power networks, the incurred connection fee and low carbon incentive are formulated for incentivizing the local energy balance and connection of renewable energy sources. Case studies testify the effectiveness for encouraging the low carbon grid connection. Energy systems are transitioning towards a decentralized and decarbonized paradigm with the integration of distributed renewable energy sources. Blockchain smart contracts have the increasing potential to facilitate the transition of energy systems due to the natures of automation, standardization, and selfenforcement. This paper proposes a Blockchain smart contracts based platform to manage the grid connection for both large scale generation companies and individual prosumers (both producers and consumers). Through evaluating the capacity margin and carbon intensity for each substation or feeder in power networks, the incurred connection fee and low carbon incentive are formulated for incentivizing the local energy balance and connection of renewable energy sources. Case studies testify the effectiveness for encouraging the low carbon grid connection

Biomedical Entity Recognition by Detection and Matching

Biomedical named entity recognition (BNER) serves as the foundation for numerous biomedical text mining tasks. Unlike general NER, BNER require a comprehensive grasp of the domain, and incorporating external knowledge beyond training data poses a significant challenge. In this study, we propose a novel BNER framework called DMNER. By leveraging existing entity representation models SAPBERT, we tackle BNER as a two-step process: entity boundary detection and biomedical entity matching. DMNER exhibits applicability across multiple NER scenarios: 1) In supervised NER, we observe that DMNER effectively rectifies the output of baseline NER models, thereby further enhancing performance. 2) In distantly supervised NER, combining MRC and AutoNER as span boundary detectors enables DMNER to achieve satisfactory results. 3) For training NER by merging multiple datasets, we adopt a framework similar to DS-NER but additionally leverage ChatGPT to obtain high-quality phrases in the training. Through extensive experiments conducted on 10 benchmark datasets, we demonstrate the versatility and effectiveness of DMNER.Comment: 9 pages content, 2 pages appendi

Numerical investigation on the dynamic response characteristics of a thermoelectric generator module under transient temperature excitations

In this work, a three-dimensional transient numerical model of a thermoelectric generator module considering the temperature-dependent properties and the topological connection of load resistance is proposed to study its dynamic response characteristics. The dynamic output power and conversion efficiency of the thermoelectric generator module under steady and different transient temperature excitations are compared and studied. A time delay exists in the output response of the thermoelectric generator module, and the time delay increases when the temperature rate increases. When the heat source temperature changes rapidly, the corresponding output power, conversion efficiency, and other thermal responses will show a more stable change trend. Moreover, the dynamic response characteristic of the output power is synchronous with that of the conversion efficiency. The periodic temperature excitation may amplify the output power, where the average output power of the sine and triangle waves are 4.93% and 2.82% respectively higher than the steady-state output power. However, the average conversion efficiency of both is almost identical to the steady-state conversion efficiency. The proposed model contributes to predicting the dynamic performance of thermoelectric generators, and can be further extended to the whole thermoelectric generator system

Blockchain technologies empowering peer‐to‐peer trading in multi‐energy systems:From advanced technologies towards applications

Algorithmic

Decoding the influence of bacterial community structure and algicidal bacteria in a Karenia longicanalis bloom event

IntroductionHarmful algal blooms (HABs) have been increasing in frequency and expanding their ranges on coastlines worldwide in recent decades. Algicidal bacteria play a pivotal role in eliminating HABs, yet the characteristics of bacterial communities and their algicidal activity during a Karenia longicanalis bloom remain poorly understood.MethodsIn this study, we investigated bacterial communities using 16S rRNA sequencing during a K. longicanalis bloom to identify bacteria with high algicidal activity that could be isolated. Five sampling sites in Tongxin Bay, located in Lianjiang County, China, including TX1 to TX5, were selected based on the concentration of K. longicanalis cells.ResultsOur 16S rRNA sequencing results revealed that the TX4 site was enriched with genera known to contain algicidal bacteria, such as Pseudoalteromonas and Alteromonas, which are members of the Gammaproteobacteria class, while Sulfitobacter, a member of the Alphaproteobacteria class, was enriched in the TX5 site. Among the 100 cultivable bacteria isolated from the 5 sampling sites, 6 exhibited an algicidal rate of over 80%, with FDHY-MQ5, isolated from the TX4 site, exhibiting an algicidal rate of approximately 100% against Karenia mikimotoi after 48 hours of challenge with 2% (v/v) bacterial volume (OD600=4.5) concentration. Our 16S rRNA sequencing result showed FDHY-MQ5 was a member of the Pseudoalteromonas genus, and this bacterium also demonstrated high algicidal activity against Heterosigma akashiwo and Alexandrium tamarense.DiscussionOur findings shed light on the changes in bacterial community structure and the algicidal behavior of bacteria towards algae during a K. longicanalis bloom, providing a research basis for a better understanding of HAB management

CASM-AMFMNet: A Network based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases

Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale fusion module net (CASM-AMFMNet) was subsequently applied for grape leaf disease identification. CoAtNet was employed as the network backbone to improve model learning and generalization capabilities, which alleviated the problem of gradient explosion to a certain extent. The CASM-AMFMNet was further utilized to capture and target grape leaf disease areas, therefore reducing background interference. Finally, Asymmetric multi-scale fusion module (AMFM) was employed to extract multi-scale features from small disease spots on grape leaves for accurate identification of small target diseases. The experimental results based on our self-made grape leaf image dataset showed that, compared to existing methods, CASM-AMFMNet achieved an accuracy of 95.95%, F1 score of 95.78%, and mAP of 90.27%. Overall, the model and methods proposed in this report could successfully identify different diseases of grape leaves and provide a feasible scheme for deep learning to correctly recognize grape diseases during agricultural production that may be used as a reference for other crops diseases

CASM-AMFMNet: A Network based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases

Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale fusion module net (CASM-AMFMNet) was subsequently applied for grape leaf disease identification. CoAtNet was employed as the network backbone to improve model learning and generalization capabilities, which alleviated the problem of gradient explosion to a certain extent. The CASM-AMFMNet was further utilized to capture and target grape leaf disease areas, therefore reducing background interference. Finally, Asymmetric multi-scale fusion module (AMFM) was employed to extract multi-scale features from small disease spots on grape leaves for accurate identification of small target diseases. The experimental results based on our self-made grape leaf image dataset showed that, compared to existing methods, CASM-AMFMNet achieved an accuracy of 95.95%, F1 score of 95.78%, and mAP of 90.27%. Overall, the model and methods proposed in this report could successfully identify different diseases of grape leaves and provide a feasible scheme for deep learning to correctly recognize grape diseases during agricultural production that may be used as a reference for other crops diseases