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ObjectiveTo explore the causes of related medical damage risks and preventive measures by analyzing the identification results of medical damage in 20 urological death cases.MethodsA retrospective analysis was conducted on 20 death cases of medical damage identification involving urology diagnosis and treatment accepted by the Judicial Appraisal Center, School of Forensic Medicine, Guizhou Medical University, and Academy of Forensic Science from 2010 to 2023.ResultsAmong the 20 cases, the male-to-female ratio was 1.5∶1. The age of the identified persons ranged from 37 to 84 years old, with an average age of 59.2 years. There were 21 medical institutions involved, with 81.0% being tertiary medical institutions. The statistical results of the causes of death showed that 60.0% deaths were due to infectious shock or hemorrhagic shock.ConclusionThe causative potency analysis of medical damage in urological death cases is closely related to the patients’ diseases, the death consequences of medical damage, and medical errors. Analyzing characteristics of typical cases can improve the comprehensive analysis ability of forensic examiners in similar cases, help them understand the rationality and compliance of medical behaviors in different situations, in order to make more accurate and comprehensive judgments.

ObjectiveTo explore the difference in CT values between pulmonary thromboembolism and postmortem clot in postmortem CT pulmonary angiography （CTPA） to further improve the application value of virtual autopsy.MethodsPostmortem CTPA data with the definite cause of death from 2016 to 2019 were collected and divided into pulmonary thromboembolism group （n=4）, postmortem clot group （n=5）, and control group （n=5）. CT values of pulmonary trunk and left and right pulmonary artery contents in each group were measured and analyzed statistically.ResultsThe average CT value in the pulmonary thromboembolism group and postmortem clot group were （168.4±53.8） HU and （282.7±78.0） HU, respectively, which were lower than those of the control group （1 193.0±82.9） HU （P

Enabling computers to understand the expressions of signers has been a challenging task that requires considering not only the temporal and spatial information of sign language videos,but also the complexity of sign language grammar.In the continuous sign language recognition task,sign language words and sign language actions share a consistent order.In contrast,in the continuous sign language translation task,the generated natural language sentences have to conform to the spoken description,and the word order may not coincide with the action order.To enable more accurate learning of signers' expressions,this paper proposes a novel deep neural network for simultaneous sign language recognition and translation.In this scheme,we explore the effectiveness of different classical pre-trained convolutional neural networks,and different multilayer temporal attention score functions on continuous sign language recognition,combined with Transformer language model,to obtain continuous sign language translation conforming to the spoken description based on continuous sign language recognition.First,this method is assessed on the first large-scale complex background Chinese continuous sign language recognition and translation dataset Tslrt.The complex contextual environment and rich action expressions of signers in Tslrt dataset are used to train our neural network model through different comparison experiments,resulting in a series of benchmark results.The best WER are 4.8% and 5.1% on the tasks of continuous sign language recognition and translation,respectively.To further demonstrate the effectiveness of our method,experiments are conducted on another Chinese continuous sign language recognition dataset Chinese-CSL and compared with other 13 methods.The results show that the WER of our method reaches 1.8%,which proves the effectiveness of the proposed method.

Objective: In order to solve the problem of inaccurate detection of alcohol accuracy due to manual "flower picking" in the segmented liquor picking process. Methods: Designed and built a segmented liquor taking system based on alcohol accuracy modeling. The research collected tuning fork frequency values, tuning fork built-in temperature values, alcohol solution temperature values and pump speed values under dynamic conditions in different modes of alcohol solutions with different concentrations, implemented adaptive tuning fork frequency filtering and dynamic compensation based on least mean square algorithm （LMS） and long short-term memory network （LSTM）, and built a liquor accuracy prediction model based on improved genetic algorithm optimized BP neural network （IGA-BP）. Results: The model outperformed the traditional genetic algorithm optimized BP neural network and BP neural network in terms of the number of iterations and prediction accuracy, and the average prediction error of the alcoholic beverages was 0.381. Conclusion: which verifies the reasonableness of the model. In order to solve the limitations of the current manual "flower picking", a method is proposed to improve the accuracy of alcohol detection in the segmented liquor taking process.

ObjectiveTo select four algorithms with relatively balanced complexity and accuracy among deep learning image classification algorithms for automatic diatom recognition, and to explore the most suitable classification algorithm for diatom recognition to provide data reference for automatic diatom testing research in forensic medicine.MethodsThe “diatom” and “background” small sample size data set （20 000 images） of digestive fluid smear of corpse lung tissue in water were built to train, validate and test four convolutional neural network （CNN） models, including VGG16, ResNet50, InceptionV3 and Inception-ResNet-V2. The receiver operating characteristic curve （ROC） of subjects and confusion matrixes were drawn, recall rate, precision rate， specificity， accuracy rate and F1 score were calculated， and the performance of each model was systematically evaluated.ResultsThe InceptionV3 model achieved much better results than the other three models with a balanced recall rate of 89.80%, a precision rate of 92.58%. The VGG16 and Inception-ResNet-V2 had similar diatom recognition performance. Although the performance of diatom recall and precision detection could not be balanced, the recognition ability was acceptable. ResNet50 had the lowest diatom recognition performance, with a recall rate of 55.35%. In terms of feature extraction, the four models all extracted the features of diatom and background and mainly focused on diatom region as the main identification basis.ConclusionIncluding the Inception-dependent model, which has stronger directivity and targeting in feature extraction of diatom. The InceptionV3 achieved the best performance on diatom identification and feature extraction compared to the other three models. The InceptionV3 is more suitable for daily forensic diatom examination.

ObjectiveTo explore the application value of virtual autopsy to obtain key evidence information on drowned corpses and its application value of virtual autopsy in the diagnosis of drowning.MethodsIn this study, 7 corpses were selected as the research objects. The image data of corpses were collected by computed tomography （CT） before conventional autopsy. The characteristics of corpses were observed through image reading, combined with virtual measurement indexes, and compared with 15 non-drowned corpses.ResultsThe postmortem CT of drowning showed the more fluid in respiratory tract than the non-drowning， and ground-glass opacities in the lung. The statistical volume of fluid in the sinus （maxillary sinus and sphenoid sinus） was （10.24±4.70） mL in drowning cases and （2.02±2.45） mL in non-drowning cases. The average CT value of fluid in the sinus， left atrial blood and gastric contents in drowning cases were （15.91±17.20）, （52.57±9.24） and （10.33±12.81） HU， respectively， which were lower than those in non-drowning cases （P

Diatom test is the main laboratory test method in the diagnosis of drowning in forensic medicine. It plays an important role in differentiating the antemortem drowning from the postmortem drowning and inferring drowning site. Artificial intelligence （AI） automatic diatom test is a technological innovation in forensic drowning diagnosis which is based on morphological characteristics of diatom, the application of AI algorithm to automatic identification and classification of diatom in tissues and organs. This paper discusses the morphological diatom test methods and reviews the research progress of automatic diatom recognition and classification involving AI algorithms. AI deep learning algorithm can assist diatom testing to obtain objective, accurate, and efficient qualitative and quantitative analysis results, which is expected to become a new direction of diatom testing research in the drowning of forensic medicine in the future.

Silicon has been a pivotal material for the next generation lithium-ion batteries, owing to its superior theoretical capacity (4200 mAh·g-1). However, the huge volume change of silicon during the process of lithiation/delithiation will lead to the instability of silicon-based anode. As a main composition of electrode, the binder plays a critical role in connecting the electrode components together and maintaining the stability of electrode, the use of an appropriate binder is essential to improve the cycling stability of silicon-based anode. Water-soluble binders which have abundant functional groups have received extensive attention due to their excellent performance in improving the electrochemical performance of silicon-based anodes. In this paper, the research progress of water-soluble binders used in silicon-based anode was reviewed. First, the properties of one-dimensional linear binders were summarized. On this basis, the research progress of composite binders with three-dimensional (3D) network structure was mainly introduced. The structure and properties of different 3D network binders were systematically analyzed. Finally, the preferred structure and properties of the water-soluble binders for silicon-based anode were proposed, and a train of thought for water-soluble binders’ choice and design was provided.

Impact electrochemistry allows for the investigation of the properties of single entities, ranging from nanoparticles (NPs) to soft bio-particles. It has introduced a novel dimension in the field of biological analysis, enhancing researchers' ability to comprehend biological heterogeneity and offering a new avenue for developing novel diagnostic devices for quantifying biological analytes. This review aims to summarize the recent advancements in impact electrochemistry-based biosensing over the past two to three years and provide insights into the future directions of this field. [ABSTRACT FROM AUTHOR]

Objective To discuss the causes and precautionary measures of the risks of relevant medical disputes by analyzing 22 cases of death from otolaryngology diseases. Methods A retrospective analysis of 22 judicial expertise cases of medical disputes related to death from otolaryngology diseases accepted by Chongqing Zhengding Judicial Appraisal Institute and Academy of Forensic Science during 2008-2018 were made. Results Of the 22 cases, the male to female ratio was 3.4∶1. The age of death was from 1.3 to 76 years old, with an average age of 45.3 years. There were 28 medical institutions involved, of which 71.4% were tertiary medical institutions. Statistics for death causes showed that respiratory asphyxia accounted for 50%. Conclusion Forensic identification provides an important scientific basis for medical disputes cases related to otolaryngology diseases. It also has an important reference value for medical institutions to optimize the work procedure of diagnosis and treatment of otolaryngology diseases and improve the quality of diagnosis and treatment.