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Pedestrian crossing intention prediction based on computer vision plays a pivotal role in enhancing the safety of autonomous driving and advanced driver assistance systems. In this paper, we present a novel multi-modal pedestrian crossing intention prediction framework leveraging the transformer model. By integrating diverse sources of information and leveraging the transformer's sequential modeling and parallelization capabilities, our system accurately predicts pedestrian crossing intentions. We introduce a novel representation of traffic environment data and incorporate lifted 3D human pose and head orientation data to enhance the model's understanding of pedestrian behavior. Experimental results demonstrate the state-of-the-art accuracy of our proposed system on benchmark datasets. [ABSTRACT FROM AUTHOR]

Based on the ideas of coordination and design chemistry, we propose a new strategy to balance the energy and sensitivity conflict by synthesizing dual anion structures in the realm of energetic complex compounds. Therefore, a novel solvent-free complex, [Ni(4-PDCA)2(NO3)2](ClO4)2 (ECCs-1), which has a six-coordinated octahedral structure and contains dual anions, was synthesized. The structure was identified by IR, EA and single-crystal XRD. To the best of our knowledge, it is the first reported dual anion structure that exhibits excellent insensitivity with acceptable thermal stability and detonation performance. [ABSTRACT FROM AUTHOR]

To synthesize lead-free primary explosives with good thermal stability, pyridine-2-carbohydrazide (2-PDCA) was used to prepare a novel energetic copper perchlorate complex (ECCs-1), which was structurally characterized by infrared spectroscopy, NMR, and elemental analysis. Its thermal decomposition properties were investigated by thermogravimetry and a synchrotron thermal analyzer, and the sensitivity was tested by the BAM method. The laser ignition experiment and the lead plate tests show that the ECCs-1 complex has a good detonation performance with a high thermal decomposition temperature (215.6 °C) and moderate sensitivity (impact sensitivity = 3 J and friction sensitivity = 5 N). [ABSTRACT FROM AUTHOR]

Nasopharyngeal carcinoma is a significant health challenge that is particularly prevalent in Southeast Asia and North Africa. MRI is the preferred diagnostic tool for NPC due to its superior soft tissue contrast. The accurate segmentation of NPC in MRI is crucial for effective treatment planning and prognosis. We conducted a search across PubMed, Embase, and Web of Science from inception up to 20 March 2024, adhering to the PRISMA 2020 guidelines. Eligibility criteria focused on studies utilizing DL for NPC segmentation in adults via MRI. Data extraction and meta-analysis were conducted to evaluate the performance of DL models, primarily measured by Dice scores. We assessed methodological quality using the CLAIM and QUADAS-2 tools, and statistical analysis was performed using random effects models. The analysis incorporated 17 studies, demonstrating a pooled Dice score of 78% for DL models (95% confidence interval: 74% to 83%), indicating a moderate to high segmentation accuracy by DL models. Significant heterogeneity and publication bias were observed among the included studies. Our findings reveal that DL models, particularly convolutional neural networks, offer moderately accurate NPC segmentation in MRI. This advancement holds the potential for enhancing NPC management, necessitating further research toward integration into clinical practice. [ABSTRACT FROM AUTHOR]

This meta-analysis investigates the prognostic value of MRI-based radiomics in nasopharyngeal carcinoma treatment outcomes, specifically focusing on overall survival (OS) variability. The study protocol was registered with INPLASY (INPLASY202420101). Initially, a systematic review identified 15 relevant studies involving 6243 patients through a comprehensive search across PubMed, Embase, and Web of Science, adhering to PRISMA guidelines. The methodological quality was assessed using the Quality in Prognosis Studies (QUIPS) tool and the Radiomics Quality Score (RQS), highlighting a low risk of bias in most domains. Our analysis revealed a significant average concordance index (c-index) of 72% across studies, indicating the potential of radiomics in clinical prognostication. However, moderate heterogeneity was observed, particularly in OS predictions. Subgroup analyses and meta-regression identified validation methods and radiomics software as significant heterogeneity moderators. Notably, the number of features in the prognosis model correlated positively with its performance. These findings suggest radiomics' promising role in enhancing cancer treatment strategies, though the observed heterogeneity and potential biases call for cautious interpretation and standardization in future research. [ABSTRACT FROM AUTHOR]

This research utilized a recently developed co-melting crystallization method to synthesize four distinct energetic coordination compounds (ECCs). The method involves the direct heating of organic ligands with inorganic salts, leading to the formation of the desired ECCs upon cooling and subsequent crystallization. This approach effectively bypasses various challenges associated with the conventional solvent-based ECC preparation process. In this study, the organic ligands employed were pyrazole and 4-chloropyrazole, while the inorganic salts included copper nitrate and copper perchlorate. A rigorous pre-experiment was meticulously designed to validate the feasibility of this method, followed by control experiments to underscore its advantages. Subsequently, comprehensive characterizations were conducted on the four ECCs, with a primary emphasis on their crystal structures and energetic properties. Notably, ECCs-3 and ECCs-4, among the synthesized ECCs, exhibit significant potential for applications in pyrotechnic materials and ignition agents. This work extends the practical applicability of the co-melting crystallization method we have developed, providing further substantiation of its advantages and potential. [ABSTRACT FROM AUTHOR]

In the field of energetic materials, high sensitivity has always been one of the obstacles restricting the practical application of energetic materials. In order to meet the practical application requirements, there are various methods to lower sensitivity, among which doping multilayer graphene is one of the important methods to reduce the friction sensitivity of energetic compounds in recent years. A multilayer graphene structure has the obvious characteristics of a weak interaction between layers and a strong interaction between atoms in layers. This special structure makes it slip between layers when it encounters friction, thus reducing the heat conduction efficiency and hindering the generation of hot spots, and finally shows the effect of lowering friction sensitivity on a macro level. Inspired by this mechanism, we designed and synthesized a ligand furan-2,5-dicarbohydrazide (FDCA) and its Ag(I)-based metal complex ([Ag(FDCA)ClO4]n, ECP-1) with a graphene-like structure. The structure of the product was confirmed using single-crystal X-ray diffraction, infrared spectroscopy and elemental analysis. ECP-1 exhibits excellent explosion performance and initiation ability, but also shows a low friction sensitivity (FS = 84 N), which is in sharp contrast with a sensitive impact sensitivity (IS = 4 J). The simple preparation method and unique structure enrich research on lowering the mechanical sensitivity of primary explosives. [ABSTRACT FROM AUTHOR]