DynLight: Realize dynamic phase duration with multi-level traffic signal control

Adopting reinforcement learning (RL) for traffic signal control (TSC) is increasingly popular, and RL has become a promising solution for traffic signal control. However, several challenges still need to be overcome. Firstly, most RL methods use fixed action duration and select the green phase for the next state, which makes the phase duration less dynamic and flexible. Secondly, the phase sequence of RL methods can be arbitrary, affecting the real-world deployment which may require a cyclical phase structure. Lastly, the average travel time and throughput are not fair metrics to evaluate TSC performance. To address these challenges, we propose a multi-level traffic signal control framework, DynLight, which uses an optimization method Max-QueueLength (M-QL) to determine the phase and uses a deep Q-network to determine the duration of the corresponding phase. Based on DynLight, we further propose DynLight-C which adopts a well-trained deep Q-network of DynLight and replace M-QL with a cyclical control policy that actuates a set of phases in fixed cyclical order to realize cyclical phase structure. Comprehensive experiments on multiple real-world datasets demonstrate that DynLight achieves a new state-of-the-art. Furthermore, the deep Q-network of DynLight can learn well on determining the phase duration and DynLight-C demonstrates high performance for deployment.Comment: 9 pages, 9 figure

Drug prescription support in dental clinics through drug corpus mining

The rapid increase in the volume and variety of data poses a challenge to safe drug prescription for the dentist. The increasing number of patients that take multiple drugs further exerts pressure on the dentist to make the right decision at point-of-care. Hence, a robust decision support system will enable dentists to make decisions on drug prescription quickly and accurately. Based on the assumption that similar drug pairs have a higher similarity ratio, this paper suggests an innovative approach to obtain the similarity ratio between the drug that the dentist is going to prescribe and the drug that the patient is currently taking. We conducted experiments to obtain the similarity ratios of both positive and negative drug pairs, by using feature vectors generated from term similarities and word embeddings of biomedical text corpus. This model can be easily adapted and implemented for use in a dental clinic to assist the dentist in deciding if a drug is suitable for prescription, taking into consideration the medical profile of the patients. Experimental evaluation of our model’s association of the similarity ratio between two drugs yielded a superior F score of 89%. Hence, such an approach, when integrated within the clinical work flow, will reduce prescription errors and thereby increase the health outcomes of patients

Drug prescription support in dental clinics through drug corpus mining

The rapid increase in the volume and variety of data poses a challenge to safe drug prescription for the dentist. The increasing number of patients that take multiple drugs further exerts pressure on the dentist to make the right decision at point-of-care. Hence, a robust decision support system will enable dentists to make decisions on drug prescription quickly and accurately. Based on the assumption that similar drug pairs have a higher similarity ratio, this paper suggests an innovative approach to obtain the similarity ratio between the drug that the dentist is going to prescribe and the drug that the patient is currently taking. We conducted experiments to obtain the similarity ratios of both positive and negative drug pairs, by using feature vectors generated from term similarities and word embeddings of biomedical text corpus. This model can be easily adapted and implemented for use in a dental clinic to assist the dentist in deciding if a drug is suitable for prescription, taking into consideration the medical profile of the patients. Experimental evaluation of our model’s association of the similarity ratio between two drugs yielded a superior F score of 89%. Hence, such an approach, when integrated within the clinical work flow, will reduce prescription errors and thereby increase the health outcomes of patients

An Efficient Method for Traffic Image Denoising

AbstractIn this paper, a novel method for traffic image denoising based on the low-rank decomposition is proposed. Firstly, the low-rank decomposition is carried out. Under the sparse and low-rank constraints of low-rank decomposition, the foreground images with complanate background and moving vehicles and the background images with similar road scene are obtained. Then the foreground image is segmented into blocks of a certain size. The variance of each block is calculated, among that the minimum is considered the estimate of the noise power. KSVD algorithm is performed for the foreground image denoising. Furthermore, the noisy pixel discrimination algorithm is performed to distinguish the noisy pixels from the noiseless pixels and the eight- neighborhood weight interpolation algorithm is performed to reconstruct the noisy pixels, where the weighted coefficients are inversely proportional to the Euclidean distances between the pixels. And PCA recovery combined with noisy pixel discrimination and eight-neighborhood weight interpolation is adopted for the background image denoising. Finally, our proposed method is conducted based on the traffic videos obtained under the same view and angle. Moreover, our proposed method is compared with several state-of-the-art denoising methods including BM3D, KSVD and PCA recovery. The experiment results illustrate that our proposed method can more effectively remove the noise, preserve the useful information and achieve a better performance in terms of both PSNR index and visual qualities

Quantitative analysis reveals increased histone modifications and a broad nucleosome-free region bound by histone acetylases in highly expressed genes in human CD4+ T cells

AbstractGenome-wide mapping of nucleosomes and histone modifications revealed meaningful patterns. Despite advances in resolving the associations between chromatin and transcription, quantitative chromatin dynamics have not been well defined. We quantitatively determined differences in histone modifications, nucleosome positions, DNA methylation, and transcription factor binding in highly expressed and repressed genes in human CD4+ T cells. We showed that the first (−1) nucleosome upstream of the transcription start site (TSS) is shifted to the 5′ direction, thus forming a broad nucleosome-free region (NFR) near the TSS in highly expressed genes in CD4+ T cells. Moreover, the transcription factor YY1 and histone acetyltransferases bind the NFR with high affinity. Most of histone acetylations drastically increase in transcription activation (>5 folds). We also suggested that single nucleotide polymorphisms (SNPs) occur at a much lower frequency in highly expressed genes than in repressed genes. Our analysis quantitatively revealed details of chromatin dynamics

AI-Driven Patient Monitoring with Multi-Agent Deep Reinforcement Learning

Effective patient monitoring is vital for timely interventions and improved healthcare outcomes. Traditional monitoring systems often struggle to handle complex, dynamic environments with fluctuating vital signs, leading to delays in identifying critical conditions. To address this challenge, we propose a novel AI-driven patient monitoring framework using multi-agent deep reinforcement learning (DRL). Our approach deploys multiple learning agents, each dedicated to monitoring a specific physiological feature, such as heart rate, respiration, and temperature. These agents interact with a generic healthcare monitoring environment, learn the patients' behavior patterns, and make informed decisions to alert the corresponding Medical Emergency Teams (METs) based on the level of emergency estimated. In this study, we evaluate the performance of the proposed multi-agent DRL framework using real-world physiological and motion data from two datasets: PPG-DaLiA and WESAD. We compare the results with several baseline models, including Q-Learning, PPO, Actor-Critic, Double DQN, and DDPG, as well as monitoring frameworks like WISEML and CA-MAQL. Our experiments demonstrate that the proposed DRL approach outperforms all other baseline models, achieving more accurate monitoring of patient's vital signs. Furthermore, we conduct hyperparameter optimization to fine-tune the learning process of each agent. By optimizing hyperparameters, we enhance the learning rate and discount factor, thereby improving the agents' overall performance in monitoring patient health status. Our AI-driven patient monitoring system offers several advantages over traditional methods, including the ability to handle complex and uncertain environments, adapt to varying patient conditions, and make real-time decisions without external supervision.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. arXiv admin note: text overlap with arXiv:2309.1057

Origin and evolution of a placental-specific microRNA family in the human genome

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are a class of short regulatory RNAs encoded in the genome of DNA viruses, some single cell organisms, plants and animals. With the rapid development of technology, more and more miRNAs are being discovered. However, the origin and evolution of most miRNAs remain obscure. Here we report the origin and evolution dynamics of a human miRNA family.</p> <p>Results</p> <p>We have shown that all members of the miR-1302 family are derived from MER53 elements. Although the conservation scores of the MER53-derived pre-miRNA sequences are low, we have identified 36 potential paralogs of MER53-derived miR-1302 genes in the human genome and 58 potential orthologs of the human miR-1302 family in placental mammals. We suggest that in placental species, this miRNA family has evolved following the birth-and-death model of evolution. Three possible mechanisms that can mediate miRNA duplication in evolutionary history have been proposed: the transposition of the MER53 element, segmental duplications and Alu-mediated recombination. Finally, we have found that the target genes of miR-1302 are over-represented in transportation, localization, and system development processes and in the positive regulation of cellular processes. Many of them are predicted to function in binding and transcription regulation.</p> <p>Conclusions</p> <p>The members of miR-1302 family that are derived from MER53 elements are placental-specific miRNAs. They emerged at the early stage of the recent 180 million years since eutherian mammals diverged from marsupials. Under the birth-and-death model, the miR-1302 genes have experienced a complex expansion with some members evolving by segmental duplications and some by Alu-mediated recombination events.</p

Gobi agriculture: an innovative farming system that increases energy and water use efficiencies. A review

International audienceAbstractIn populated regions/countries with fast economic development, such as Africa, China, and India, arable land is rapidly shrinking due to urban construction and other industrial uses for the land. This creates unprecedented challenges to produce enough food to satisfy the increased food demands. Can the millions of desert-like, non-arable hectares be developed for food production? Can the abundantly available solar energy be used for crop production in controlled environments, such as solar-based greenhouses? Here, we review an innovative cultivation system, namely “Gobi agriculture.” We find that the innovative Gobi agriculture system has six unique characteristics: (i) it uses desert-like land resources with solar energy as the only energy source to produce fresh fruit and vegetables year-round, unlike conventional greenhouse production where the energy need is satisfied via burning fossil fuels or electrical consumption; (ii) clusters of individual cultivation units are made using locally available materials such as clay soil for the north walls of the facilities; (iii) land productivity (fresh produce per unit land per year) is 10–27 times higher and crop water use efficiency 20–35 times greater than traditional open-field, irrigated cultivation systems; (iv) crop nutrients are provided mainly via locally-made organic substrates, which reduce synthetic inorganic fertilizer use in crop production; (v) products have a lower environmental footprint than open-field cultivation due to solar energy as the only energy source and high crop yields per unit of input; and (vi) it creates rural employment, which improves the stability of rural communities. While this system has been described as a “Gobi-land miracle” for socioeconomic development, many challenges need to be addressed, such as water constraints, product safety, and ecological implications. We suggest that relevant policies are developed to ensure that the system boosts food production and enhances rural socioeconomics while protecting the fragile ecological environment