Distribution Route Optimization for Electric Vehicles in Urban Cold Chain Logistics for Fresh Products under Time-Varying Traffic Conditions

Electric vehicles (EVs) have been widely used in urban cold chain logistic distribution and transportation of fresh products. In this paper, an electric vehicle routing problem (EVRP) model under time-varying traffic conditions is designed for planning the itinerary for fresh products in the urban cold chain. The object of the EVRP model is to minimize the total cost of logistic distribution that includes economic cost and fresh value loss cost. To reflect the real situation, the EVRP model considers several influencing factors, including time-varying road network traffic, road type, client’s time-window requirement, freshness of fresh products, and en route queuing for charging. Furthermore, to address the EVRP, an improved adaptive ant colony algorithm is designed. Simulation test results show that the proposed method can allow EVs to effectively avoid traffic congestion during the distribution process, reduce the total distribution cost, and improve the performance of the cold chain logistic distribution process for fresh products. Document type: Articl

Using Pre-trained Language Models for Toxic Comment Classification

Toxic comment classification is a core natural language processing task for combating online toxic comments. It follows the supervised learning paradigm which requires labelled data for the training. A large amount of high-quality training data is empirically beneficial to the model performance. Transferring a pre-trained language model (PLM) to a downstream model allows the downstream model to access more data without creating new labelled data. Despite the increasing research on PLMs in NLP tasks, there remains a fundamental lack of understanding in applying PLMs to toxic comment classification. This work focuses on this area from three perspectives. First, we investigate different transferring strategies for toxic comment classification tasks. We highlight the importance of efficiency during the transfer. The transferring efficiency seeks a reasonable requirement of computational resources and a comparable model performance at the same time. Thus, we explore the continued pre-training in-domain which further pre-trains a PLM with in-domain corpus. We compare different PLMs and different settings for the continued pre-training in-domain. Second, we investigate the limitations of PLMs for toxic comment classification. Taking the most popular PLM, BERT, as the representative model for our study, we focus on studying the identity term bias (i.e. prediction bias towards comments with identity terms, such as "Muslim" and "Black"). To investigate the bias, we conduct both quantitative and qualitative analyses and study the model explanations. We also propose a hypothesis that builds on the potential relationship between the identity term bias and the subjectivity of comments. Third, building on the hypothesis, we propose a novel BERT-based model to mitigate the identity term bias. Our method is different from previous methods that try to suppress the model's attention to identity terms. To do so, we insert the subjectivity into the model along with the suggestion of the presence of identity terms. Our method shows consistent improvements on a range of different toxic comment classification tasks

The History and Outlook of Animal Drugs Treating Asthma, Chronic Bronchitis, and Haze Episode-induced Respiratory Diseases

Animal drugs have been historically applied in Chinese remedies for more than two thousands. It was reported that Chinese medical animals consisting of 1,590 species took up 12.5% of the total number of all TCM resources. Those animal drugs such as, earthworm, gecko, periostracum cicadae, and scorpios, of commonly used in China, are very remarkable and traditional for the treatment of asthma or chronic bronchitis. This review presents research advance of animal drugs possessing significant implications for the development of novel anti-asthma or chronic bronchitis drugs. The experimental studies and clinical efficacies against asthma and chronic bronchitis of animal drugs were summarized herein. Moreover, the potential utilization of animal drugs on inhibiting haze/fog induced respiratory diseases was also discusse

Whole-lesion histogram analysis of multiple diffusion metrics for differentiating lung cancer from inflammatory lesions

BackgroundWhole-lesion histogram analysis can provide comprehensive assessment of tissues by calculating additional quantitative metrics such as skewness and kurtosis; however, few studies have evaluated its value in the differential diagnosis of lung lesions.PurposeTo compare the diagnostic performance of conventional diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) and diffusion kurtosis imaging (DKI) in differentiating lung cancer from focal inflammatory lesions, based on whole-lesion volume histogram analysis.MethodsFifty-nine patients with solitary pulmonary lesions underwent multiple b-values DWIs, which were then postprocessed using mono-exponential, bi-exponential and DKI models. Histogram parameters of the apparent diffusion coefficient (ADC), true diffusivity (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f), apparent diffusional kurtosis (Kapp) and kurtosis-corrected diffusion coefficient (Dapp) were calculated and compared between the lung cancer and inflammatory lesion groups. Receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic performance.ResultsThe ADCmean, ADCmedian, Dmean and Dmedian values of lung cancer were significantly lower than those of inflammatory lesions, while the ADCskewness, Kappmean, Kappmedian, KappSD, Kappkurtosis and Dappskewness values of lung cancer were significantly higher than those of inflammatory lesions (all p < 0.05). ADCskewness (p = 0.019) and Dmedian (p = 0.031) were identified as independent predictors of lung cancer. Dmedian showed the best performance for differentiating lung cancer from inflammatory lesions, with an area under the ROC curve of 0.777. Using a Dmedian of 1.091 × 10-3 mm2/s as the optimal cut-off value, the sensitivity, specificity, positive predictive value and negative predictive value were 69.23%, 85.00%, 90.00% and 58.62%, respectively.ConclusionsWhole-lesion histogram analysis of DWI, IVIM and DKI parameters is a promising approach for differentiating lung cancer from inflammatory lesions, and Dmedian shows the best performance in the differential diagnosis of solitary pulmonary lesions

ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data

Radiology report generation, as a key step in medical image analysis, is critical to the quantitative analysis of clinically informed decision-making levels. However, complex and diverse radiology reports with cross-source heterogeneity pose a huge generalizability challenge to the current methods under massive data volume, mainly because the style and normativity of radiology reports are obviously distinctive among institutions, body regions inspected and radiologists. Recently, the advent of large language models (LLM) offers great potential for recognizing signs of health conditions. To resolve the above problem, we collaborate with the Second Xiangya Hospital in China and propose ChatRadio-Valuer based on the LLM, a tailored model for automatic radiology report generation that learns generalizable representations and provides a basis pattern for model adaptation in sophisticated analysts' cases. Specifically, ChatRadio-Valuer is trained based on the radiology reports from a single institution by means of supervised fine-tuning, and then adapted to disease diagnosis tasks for human multi-system evaluation (i.e., chest, abdomen, muscle-skeleton, head, and maxillofacial $\&$ neck) from six different institutions in clinical-level events. The clinical dataset utilized in this study encompasses a remarkable total of \textbf{332,673} observations. From the comprehensive results on engineering indicators, clinical efficacy and deployment cost metrics, it can be shown that ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al., in terms of the diseases diagnosis from radiology reports. ChatRadio-Valuer provides an effective avenue to boost model generalization performance and alleviate the annotation workload of experts to enable the promotion of clinical AI applications in radiology reports

Economic Policy Uncertainty and ESG Performance: Evidence from China

Using the data of listed companies in Chinese A-share market from 2011 to 2020, this paper investigates the effect of the economic policy uncertainty (EPU) on corporate environmental, social, and governance (ESG). The results show that during periods of high economic policy uncertainty, firms increase their overall ESG performance, corporate environmental performance, social performance and governance performance. Heterogeneous analyses show that the positive effect of EPU on ESG performance is more pronounced for state-owned enterprises, for firms with better corporate governance, for firms with more institutional investors, and for firms with less financing constraints. This study contributes to the literature on the determinants of ESG and provides implications for both practitioners and academics

Design of novel intrinsically safe power supply with 'ia' grade

According to working condition of mine-used power supply in coal mine underground, novel intrinsically safe power supply with 'ia' grade was designed. The power supply uses a fully enclosed AC-DC module to eliminate special flameproof enclosure, and has over-voltage protection circuit of dual redundancy and over-current protection circuit of three redundancy. The experimental results show there is no spark when the power supply load is short circuited under the conditions of no fault, one counting fault and two counting faults, which realizes intrinsic safety

Optimization of transportation routing problem for fresh food in time-varying road network: Considering both food safety reliability and temperature control.

Study on fresh food safety reliability and temperature control has being a research focus in the fresh food cold distribution optimization study field. On this basis, optimization of transportation routing problem with time windows for fresh food in time-varying road network is studied by considering both economic cost and fresh food safety loss. A calculation method for path division strategy is designed. A food safety value loss measurement function, a metric function of energy and heat conversion a measure function of carbon emission rate are employed by considering time-varying vehicle speeds, fuel consumptions, cost of temperature control, the loss of food safety reliability and carbon emissions from transportation and temperature control. The fresh food cold chain distribution vehicle routing problem model with time windows in time-varying road network is formulated based on the objective of the distribution cost and food safety value loss minimization. According to the characteristics of the model, an adaptive improved ant colony algorithm is designed. Finally, the experimental data show that the model can effectively avoid the congestion period, reasonably control the refrigeration temperature, reduce the distribution cost, and improve food safety