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Controlling crop diseases and pests is essential for intelligent agriculture (IA) due to the significant reduction in crop yield and quality caused by these problems. In recent years, the remote sensing (RS) areas has been prevailed over by unmanned aerial vehicle (UAV)-based applications. Herein, by using methods such as keyword co-contribution analysis and author co-occurrence analysis in bibliometrics, we found out the hot-spots of this field. UAV platforms equipped with various types of cameras and other advanced sensors, combined with artificial intelligence (AI) algorithms, especially for deep learning (DL) were reviewed. Acknowledging the critical role of comprehending crop diseases and pests, along with their defining traits, we provided a concise overview as indispensable foundational knowledge. Additionally, some widely used traditional machine learning (ML) algorithms were presented and the performance results were tabulated to form a comparison. Furthermore, we summarized crop diseases and pests monitoring techniques using DL and introduced the application for prediction and classification. Take it a step further, the newest and the most concerned applications of large language model (LLM) and large vision model (LVM) in agriculture were also mentioned herein. At the end of this review, we comprehensively discussed some deficiencies in the existing research and some challenges to be solved, as well as some practical solutions and suggestions in the near future.

Stimulus-responsive drug delivery systems generally aim to release the active pharmaceutical ingredient (API) in response to specific conditions and have recently been explored for disease treatments. These approaches can also be extended to molecular imaging to report on disease diagnosis and management. The stimuli used for activation are based on differences between the environment of the diseased or targeted sites, and normal tissues. Endogenous stimuli include pH, redox reactions, enzymatic activity, temperature and others. Exogenous site-specific stimuli include the use of magnetic fields, light, ultrasound and others. These endogenous or exogenous stimuli lead to structural changes or cleavage of the cargo carrier, leading to release of the API. A wide variety of stimulus-responsive systems have been developed—responsive to both a single stimulus or multiple stimuli—and represent a theranostic tool for disease treatment. In this review, stimuli commonly used in the development of theranostic nanoplatforms are enumerated. An emphasis on chemical structure and property relationships is provided, aiming to focus on insights for the design of stimulus-responsive delivery systems. Several examples of theranostic applications of these stimulus-responsive nanomedicines are discussed.

Chemotherapy resistance represents a formidable obstacle in oncological therapeutics, substantially compromising the efficacy of adjuvant chemotherapy regimens and contributing to unfavorable clinical prognoses. Emerging evidence has elucidated the pivotal involvement of exosomes in the dissemination of chemoresistance phenotypes among tumor cells and within the tumor microenvironment. This review delineates two distinct intra-tumoral resistance mechanisms orchestrated by exosomes: (1) the exosome-mediated sequestration of chemotherapeutic agents coupled with enhanced drug efflux in neoplastic cells, and (2) the horizontal transfer of chemoresistance to drug-sensitive cells through the delivery of bioactive molecular cargo, thereby facilitating the propagation of resistance phenotypes across the tumor population. Furthermore, the review covers current in vivo experimental data focusing on targeted interventions against specific genetic elements and exosomal secretion pathways, demonstrating their potential in mitigating chemotherapy resistance. Additionally, the therapeutic potential of inhibiting exosome-mediated transporter transfer strategy is particularly examined as a promising strategy to overcome tumor resistance mechanisms.

The proposed Type-Guided Expert Model-based Fastener Detection and Diagnosis framework (TGEM-FDD) framework is particularly suitable for automated visual inspection systems in railway infrastructure maintenance. It can be deployed on track inspection vehicles or drones to enable real-time, high-precision detection of diverse fastener types and multiple concurrent defects along railway lines. This system provides a practical solution for railway authorities to transition from manual, periodic inspections to continuous, intelligent monitoring, thereby enhancing operational safety while reducing labor costs and human error in maintenance operations. Railway track fasteners, serving as critical connecting components, have a reliability that directly impacts railway operational safety. To address the performance bottlenecks of existing detection methods in handling complex scenarios with diverse fastener types and co-occurring multiple defects, this paper proposes a Type-Guided Expert Model-based Fastener Detection and Diagnosis framework (TGEM-FDD) based on You Only Look Once (YOLO) v8. This framework follows a “type-identification-first, defect-diagnosis-second” paradigm, decoupling the complex task: the first stage employs an enhanced YOLOv8s with Deepstar, SPPF-attention, and DySample (YOLOv8s-DSD) detector integrating Deepstar Block, Spatial Pyramid Pooling Fast with Attention (SPPF-Attention), and Dynamic Sample (DySample) modules for precise fastener localization and type identification; the second stage dynamically invokes a specialized multi-label classification “expert model” based on the identified type to achieve accurate diagnosis of multiple defects. This study constructs a multi-label fastener image dataset containing 4800 samples to support model training and validation. Experimental results demonstrate that the proposed YOLOv8s-DSD model achieves a remarkable 98.5% mean average precision at an Intersection over Union threshold of 0.5 (mAP@0.5) in the first-stage task, outperforming the original YOLOv8s baseline and several mainstream detection models. In end-to-end system performance evaluation, the TGEM-FDD framework attains a comprehensive Task mean average precision (Task mAP) of 88.1% and a macro-average F1 score for defect diagnosis of 86.5%, significantly surpassing unified single-model detection and multi-task separate-head methods. This effectively validates the superiority of the proposed approach in tackling fastener type diversity and defect multi-label complexity, offering a viable solution for fine-grained component management in complex industrial scenarios.

This paper reports a comparative study on the high temperature pyrolysis characteristics of three C9H12 isomers, including n-propylbenzene (PBZ), 1,3,5-trimethylbenzene (T135MBZ), and 1,2,4-trimethylbenzene (T124MBZ), via single-pulse shock tube (SPST) experiments and kinetic simulations. The SPST experiments were conducted in the temperature range of 1100–1700 K, at pressures of 10 bar and 15 bar, with a fixed fuel concentration of 200 ppm. The reaction time was approximately 1.8 ms for all of the experiments. The distributions of the pyrolysis products were quantitatively analyzed as functions of pressure and temperature. A detailed kinetic mechanism was used to simulate the experimental results, and it is demonstrated that the mechanism can capture the pyrolysis characteristics reasonably well. Both experimental and simulation results reveal that PBZ exhibits higher fuel reactivity than T124MBZ and T135MBZ under the studied conditions. Pyrolysis of all three C9H12 isomers generates key soot precursors, including acetylene and benzene. Sensitivity and rate-of-production (ROP) analyses indicate similar primary pyrolysis pathways. The benzyl radical is first formed through the dehydrogenation reaction and then it undergoes a series of decomposition reactions leading to the detected small hydrocarbon species. This study not only provides an in-depth understanding of the high temperature pyrolysis characteristics of the three C9H12 isomers, but also provides essential validation data for the development and optimization of chemical kinetic mechanisms for alkyl aromatic hydrocarbons.

Minor displacements and deflections of rail fastener clips pose a critical risk to railway safety, which are difficult to quantify accurately using traditional object detection methods. This paper proposes an improved YOLOv8-pose-based method, You Only Look Once version 8-pose with GAM, SPPF-Attention, and Wise-IoU (YOLOv8-pose-GSW) for automated and quantitative pose detection of fastener clips. Firstly, a high-precision keypoint detection network is constructed by integrating a Global Attention Mechanism (GAM) into the neck, enhancing the Spatial Pyramid Pooling Fast (SPPF) module to Spatial Pyramid Pooling Fast with Attention (SPPF-Attention) in the backbone, and adopting the Wise Intersection over Union (Wise-IoU) loss function. Subsequently, a posterior verification mechanism based on spatial constraint error is designed to eliminate unreliable detections by leveraging the inherent geometric priors of fasteners. Finally, the deflection angle, longitudinal displacement, and lateral displacement of the clip are calculated from the verified keypoints. Experimental results demonstrate that the proposed method achieves an Average Precision at IoU threshold from 0.5 to 0.95 (AP@0.5:0.95) of 77.5%, representing a 3.6% improvement over the baseline YOLOv8s-pose model, effectively balancing detection accuracy and computational efficiency. This work provides a reliable technical solution for the refined maintenance of rail fasteners.

In recent years, with the rapid development of various cities, the operation and maintenance of the public transport system has become increasingly prominent problems, the development of the intelligent public transport system has been imperative. This paper takes the intelligent public transport system as the research object, using wireless network transmission technology, big data retrieval and processing, cloud computing platform and other technologies, analyzes the main requirements of the intelligent public transport system. This system adopts the classic three-layer architecture of the Internet of Things, namely, the perception layer, the network layer and the platform layer. Intelligent public transport dispatching system is the core system of intelligent public transport. The paper uses frog leaping algorithm as the dispatching algorithm to realize the function of public transport dispatching. Through the test, it can complete the task of adding or reducing vehicles according to the increase or decrease of passenger flow. The design and implementation of the cloud platform provides a strong support for the renewal and development of modern urban public transportation system, and has a certain guiding significance. As a complete system with more rapid and accurate management, more convenient and efficient operation, it improves the public transport dispatching ability and improves the travel experience of passengers, and has a cross-era significance for the development of urban public transportation.

The identification of strong gravitational lenses is essential to facilitate many studies in astronomy. The search for strong gravitational lenses has become more challenging because of their scientific value and their rarity. In this paper, we construct a data set for strong gravitational lensing searches that consist of known lenses and lens candidates from the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys (the DESI Legacy Imaging Surveys) and the Dark Energy Survey (DES) and non-lenses from the Legacy Surveys Data Release 9 (DR9). We apply the YOLOX algorithm as the basic framework and improve it by selecting the optimal optimizer, activation function, attention mechanism, and loss function. The improved YOLOX-LS algorithm achieves 97.87%, 97.51%, 0.97, 96.8%, and 53.1% in the evaluation metrics of precision, recall, F1 score, mean average precision (mAP)@0.5, and mAP@0.5:0.95, respectively. Compared with the YOLOX model, it improves by 0.63%, 0.26%, and 0.6% in the three metrics of precision, recall, and mAP@0.5, respectively. This paper presents the results of the trained YOLOX-LS algorithm applied to 4.75 million cutout images. These images are centered on the central source with magz ≤ 20 in the Dark Energy Camera Legacy Survey footprint from DESI DR9. Finally, we find 1697 lenses, including 303 known lenses or candidates, and 1394 new candidates, among which there are 102 high-quality candidates. This further verifies that the YOLOX-LS algorithm proposed in this paper can be effectively applied to the search for strong gravitational lenses. All visual results are displayed online at https://github.com/Young-mmm/YOLOX-LS.

Exposure to bisphenol A (BPA), a monomer widely used to manufacture polycarbonate plastics, has been reported to be associated with abnormalities of liver function and hepatic damage. However, the molecular mechanism under the pathogenesis of hepatic injury is unclear. In this study, the effect of perinatal exposure to BPA at the reference dose of 50 µg/kg/day on the apoptotic index in the liver of rat offspring was investigated. Increased levels of ALT and enhanced cell apoptosis were observed in the liver of rat offspring at 15 and 21 weeks, and significantly increased activity of caspase-3 and caspase-9 and elevated levels of cytochrome c were also confirmed. In addition, significant change in the expression levels of Bcl-2 and Bax were found in BPA-treated offspring at 21 weeks. For in vitro experiments, liver mitochondria were isolated from neonatal rats and were treated with BPA. BPA treatment led to a significant increase in mitochondrial permeability transition. Moreover, the supernatant from BPA-treated mitochondria significantly increased apoptotic changes in nuclei isolated from liver tissue. In conclusion, the study demonstrates that BPA induces mitochondria-mediated apoptosis in hepatic cells, which may contribute to long-term hepatotoxicity induced by early-life exposure to BPA.

An electricity substitution strategy that replaces fossil fuels such as coal and oil with electricity in end-use energy consumption, can effectively contribute to an energy transition and the early achievement of carbon peaking and carbon neutrality targets. As the benefits of electricity substitution are not synchronized across China’s regions, this paper uses a three-stage data envelopment analysis (DEA) model to measure the efficiency of electric energy substitution in 30 provinces of China in 2017. The results show that both environmental factors and random errors have significant effects on energy efficiency. After eliminating these influences, the efficiency of electrical energy substitution among regions presented the following pattern: “high in the east and low in the west”. According to the evaluation results, this paper proposes corresponding suggestions for the development of electrical energy substitution.