Deep Cost-sensitive Learning for Wheat Frost Detection

Frost damage is one of the main factors leading to wheat yield reduction. Therefore, the detection of wheat frost accurately and efficiently is beneficial for growers to take corresponding measures in time to reduce economic loss. To detect the wheat frost, in this paper we create a hyperspectral wheat frost data set by collecting the data characterized by temperature, wheat yield, and hyperspectral information provided by the handheld hyperspectral spectrometer. However, due to the imbalance of data, that is, the number of healthy samples is much higher than the number of frost damage samples, a deep learning algorithm tends to predict biasedly towards the healthy samples resulting in model overfitting of the healthy samples. Therefore, we propose a method based on deep cost-sensitive learning, which uses a one-dimensional convolutional neural network as the basic framework and incorporates cost-sensitive learning with fixed factors and adjustment factors into the loss function to train the network. Meanwhile, the accuracy and score are used as evaluation metrics. Experimental results show that the detection accuracy and the score reached 0.943 and 0.623 respectively, this demonstration shows that this method not only ensures the overall accuracy but also effectively improves the detection rate of frost samples.Comment: 7 pages, 4 figures, accepted by ICBAIE 202

Online Bus Speed Prediction With Spatiotemporal Interaction: A Laplace Approximation-Based Bayesian Approach

This study proposes a novel Bayesian hierarchical approach for online bus speed prediction by explicitly accounting for the spatiotemporal interaction (STI) of speed observations. The use of Laplace approximation can expedite the estimation of Bayesian models and enable the implementation of online prediction. Large numbers of trials are carried out to identify significant predictors and the optimal length of the look-back time window to achieve the highest prediction accuracy. The spatiotemporal interacting patterns are also explored, and results show that the Type IV model assuming the structured spatial effect interacts with the structured temporal effect can best accommodate the bus speed data. Besides, prediction errors of the Type IV model randomly distribute over time and space. The proposed model can achieve high prediction accuracy and computational efficiency without compromising the interpretability of the contributing factors and the unobserved spatiotemporal heterogeneity. The proposed model can be used to assist public transit operation and management, such as bus scheduling, congestion warning, and the development of proactive measures to mitigate bus delays

Identification of DNA-protein binding residues through integration of Transformer encoder and Bi-directional Long Short-Term Memory

DNA-protein binding is crucial for the normal development and function of organisms. The significance of accurately identifying DNA-protein binding sites lies in its role in disease prevention and the development of innovative approaches to disease treatment. In the present study, we introduce a precise and robust identifier for DNA-protein binding residues. In the context of protein representation, we combine the evolutionary information of the protein, represented by its position-specific scoring matrix, with the spatial information of the protein's secondary structure, enriching the overall informational content. This approach initially employs a combination of Bi-directional Long Short-Term Memory and Transformer encoder to jointly extract the interdependencies among residues within the protein sequence. Subsequently, convolutional operations are applied to the resulting feature matrix to capture local features of the residues. Experimental results on the benchmark dataset demonstrate that our method exhibits a higher level of competitiveness when compared to contemporary classifiers. Specifically, our method achieved an MCC of 0.349, SP of 96.50%, SN of 44.03% and ACC of 94.59% on the PDNA-41 dataset

An Fe stabilized metallic phase of NiS2 for the highly efficient oxygen evolution reaction.

This work reports a fundamental study on the relationship of the electronic structure, catalytic activity and surface reconstruction process of Fe doped NiS2 (FexNi1-xS2) for the oxygen evolution reaction (OER). A combined photoemission and X-ray absorption spectroscopic study reveals that Fe doping introduces more occupied Fe 3d6 states at the top of the valence band and thereby induces a metallic phase. Meanwhile, Fe doping also significantly increases the OER activity and results in much better stability with the optimum found for Fe0.1Ni0.9S2. More importantly, we performed detailed characterization to track the evolution of the structure and composition of the catalysts after different cycles of OER testing. Our results further confirmed that the catalysts gradually transform into amorphous (oxy)hydroxides which are the actual active species for the OER. However, a fast phase transformation in NiS2 is accompanied by a decrease of OER activity, because of the formation of a thick insulating NiOOH layer limiting electron transfer. On the other hand, Fe doping retards the process of transformation, because of a shorter Fe-S bond length (2.259 Å) than Ni-S (2.400 Å), explaining the better electrochemical stability of Fe0.1Ni0.9S2. These results suggest that the formation of a thin surface layer of NiFe (oxy)hydroxide as an active OER catalyst and the remaining Fe0.1Ni0.9S2 as a conductive core for fast electron transfer is the base for the high OER activity of FexNi1-xS2. Our work provides important insight and design principle for metal chalcogenides as highly active OER catalysts

Detection of early-universe gravitational-wave signatures and fundamental physics

Detection of a gravitational-wave signal of non-astrophysical origin would be a landmark discovery, potentially providing a significant clue to some of our most basic, big-picture scientific questions about the Universe. In this white paper, we survey the leading early-Universe mechanisms that may produce a detectable signal—including inflation, phase transitions, topological defects, as well as primordial black holes—and highlight the connections to fundamental physics. We review the complementarity with collider searches for new physics, and multimessenger probes of the large-scale structure of the Universe.Peer reviewe

Detection of early-universe gravitational-wave signatures and fundamental physics

Detection of a gravitational-wave signal of non-astrophysical origin would be a landmark discovery, potentially providing a significant clue to some of our most basic, big-picture scientific questions about the Universe. In this white paper, we survey the leading early-Universe mechanisms that may produce a detectable signal—including inflation, phase transitions, topological defects, as well as primordial black holes—and highlight the connections to fundamental physics. We review the complementarity with collider searches for new physics, and multimessenger probes of the large-scale structure of the Universe.Peer reviewe

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Coordinated analysis of county geological environment carrying capacity and sustainable development under remote sensing interpretation combined with integrated model

Geological environment carrying capacity (GECC) is the key to regional sustainable development (RSD). The study has become an international heat due to frequent global geological disasters, which have resulted in socio-economic losses, weakened GECC. Therefore, this study combined with quantity of information (QI), random forest (RF) and XGBoost (XGB) algorithms, constructed a GECC assessment model in Xiuyan, China based on remote sensing (RS) and geographic information system (GIS) techniques. The validity of the model was verified by disaster information in two period. The results reveal that the indicator system of RS image can reflect the geological factors’ influence on GECC more truthfully, and avoided duplication of indicators. The correlation coefficients of the indicators were all less than 0.9, which showed the validity of the indicator system. The QI_RF model performed best with high accuracy (0.96). The mean absolute error (MAE) is 0.098. The disaster management cost (DMC), elevation and rainfall were the main indicators with weights over 0.1. The GECC in study area is mainly balanced. Although the overloaded area was smallest, it was the key area to limit the RSD, which had been improved by adjusting the land or human activities planning. Compared with the results of geological disasters in two period, the disasters in overloaded area were reduced by 58%, which showed that the improvement measures under this model were effective. The method of GECC assessment based on RS and integrated model proposed in this study takes into account the common indicators that affect the development of geological disasters. It can provide reference for RSD under the influence of geological disasters and has universal applicability

Electric bus charging facility planning with uncertainties: Model formulation and algorithm design

This paper investigates the electric bus charging facility planning (EB-CFP) problem for a bus transit company operating a heterogeneous electric bus (EB) fleet to provide public transportation services, taking into account uncertainties in both EB travel time and battery degradation. The goal of the EB-CFP problem is to determine the number and type of EB chargers that should be deployed at bus terminals and depots to meet daily EB charging demand while minimizing total cost. The problem is formulated as a two-stage stochastic programming model, with the first stage determining the EB charger deployment scheme and the second stage estimating the EB daily operational cost with respect to a predetermined EB trip timetable for a given EB charger deployment scheme. To effectively address the second stage problem, a multi-agent EB transit simulation system that mimics the daily EB operation process is developed. We then design two heuristic methods, the reinforcement learning (RL)-based method and the surrogate-based optimization (SBO), that use the developed multi-agent EB transit simulation system to solve the two-stage stochastic programming model for large-scale instances. Lastly, we run a series of experiments on a fictitious EB transit network and a real-world EB transit network in Singapore to evaluate the performance of the models and algorithms. In order to improve the performance of the EB transit system, some managerial insights are also provided to urban bus transit companies