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Abstract Objective To analyze the clinical characteristics of esophageal button battery impactions in children and explore safe and effective treatment methods. Methods This retrospective cohort study was conducted at a single tertiary care center, Shenzhen Children’s Hospital, encompassing 89 children diagnosed with esophageal button battery impactions between January 2013 and January 2023. To minimize esophageal mucosal corrosion, prompt removal of the button battery with a first-aid fast track rigid esophagoscopy under general anesthesia was performed within thirty minutes of diagnosis. The clinical features and complications were recorded and analyzed. Results Button battery as esophageal foreign body was prevalent among children under 3 years old (79.8%), with boys exhibiting a higher incidence rate (56.2%) compared to girls (43.8%), and an average age of 25.8 months. The median duration from ingestion to hospital admission was 3 h (range: 0.5 h to 3 months). Common symptoms included vomiting and dysphagia, with early stage vomiting of brown foamy secretions being a characteristic presentation of esophageal button battery impactions. The majority (77.5%) of batteries were lodged in the upper esophagus. The larger batteries were verified to be more prone to complications. All 89 cases exhibited varying degrees of esophageal mucosal erosion, with 31 cases (34.8%) experiencing severe complications, including esophageal stenosis in 11 cases (35.5%), esophageal perforation in 9 cases (29%) with 4 cases of tracheoesophageal fistula, vocal cord paralysis in 6 cases (19.4%), hemorrhage in 2 cases (6.5%), mediastinitis in 2 cases (6.5%), and periesophageal abscess in 1 case (3.2%). Despite the severity of these complications, none of the patients died after emergency surgery. Conclusion Esophageal button battery impactions can lead to significant damage to the esophageal mucosa due to its strong corrosiveness. Prompt action is crucial to mitigate the risk of complications. For the first time, we implement a first-aid fast track surgical intervention following diagnosis is imperative to minimize the incidence of adverse outcomes.

Abstract Background To explore the feasibility and effectiveness of applying CBL teaching method and SEGUE Framework in the doctor-patient communication skills of resident physicians in the department of otolaryngology. Methods This is an observational study to compare the score changes in doctor-patient communication skills of 120 resident physicians, before and after using CBL combined SEGUE Framework teaching method. The effects of gender, age, grade, educational background and marital status on SEGUE score were analyzed. Results Through the combined application of CBL teaching method and SEGUE Framework, the SEGUE score of 120 resident physicians was significantly improved. There was no significant difference in SEGUE score among different sex and marital status of resident physicians. SEGUE score is positively correlated with age; Different grades and educational backgrounds have significant effects on SEGUE score. Conclusion The combination of CBL teaching method and SEGUE Framework is feasible and effective in the education program of doctor-patient communication skills for resident physicians in the department of otolaryngology, and worthy of popularization and application in other medical specialties.

Auditory Brainstem Response (ABR) is an evoked potential in the brainstem’s neural centers in response to sound stimuli. Clinically, characteristic waves, especially Wave V latency, extracted from ABR can objectively indicate auditory loss and diagnose diseases. Several methods have been developed for the extraction of characteristic waves. To ensure the effectiveness of the method, most of the methods are time-consuming and rely on the heavy workloads of clinicians. To reduce the workload of clinicians, automated extraction methods have been developed. However, the above methods also have limitations. This study introduces a novel deep learning network for automatic extraction of Wave V latency, named ABR-Attention. ABR-Attention model includes a self-attention module, first and second-derivative attention module, and regressor module. Experiments are conducted on the accuracy with 10-fold cross-validation, the effects on different sound pressure levels (SPLs), the effects of different error scales and the effects of ablation. ABR-Attention shows efficacy in extracting Wave V latency of ABR, with an overall accuracy of $96.76~\pm ~0.41$ % and an error scale of 0.1ms, and provides a new solution for objective localization of ABR characteristic waves.

Micro-corona devices could be extensively utilized in gas sensing, switchgear, biomedicine, and other fields. As the influence mechanism of the cathode curvature radius on micro-corona discharge dynamical processes is very important for performance optimization and the promotion of these devices, a micro-scale corona discharge gas model in a mixture of N2-O2 is proposed based on the fluid–chemical mixing method, which describes the dynamic process of the discharge at atmosphere and normal temperatures. To reveal the influence mechanism of the nanowire curvature radius on the micro-corona discharge, the effect of the cathode nanowire radius on the discharge current, electric field, ionization reaction rate, and charged particle characteristics at different gaps and voltages were determined. The findings indicate that the effect of curvature radius on discharge intensity varies under different gap and voltage conditions. Further analysis indicates that an increase in curvature radius reduces the electric field near the tip while increasing the ionization area and secondary emission area as well as the number of positive ions in the space, consequently affecting the coupling process between the collision ionization and the secondary emission. Especially under the conditions of either small gap or low voltage, a suitable increase in the curvature radius could promote the coupling process and then increase the discharge current.

Accurate short-term load forecasting of distribution transformer in extreme weather will effectively assist power dispatching and enable safe and stable operation of power grid. Therefore, this paper proposes the Bagging–XGBoost algorithm based extreme weather identification and short-term load forecasting model, which can warn the time period and detailed value of peak load in advance. Firstly, based on Extreme Gradient Boosting (XGBoost) algorithm, the idea of Bagging is introduced to reduce the output variance and enhance the generalization ability of the algorithm. Then, the mutual information (MI) between weather influencing factors and load is analyzed to adjust the input weight of the model and improve its ability to track weather changes. Next, considering the load, weather and time factors, the extreme weather identification model is established to determine the occurrence range of peak load. Finally, the specialized training set is selected based on the weighted similarity, and high-accuracy short-term load forecasting model is established. Compared with the traditional model, the model proposed in this paper reduces the average Mean Absolute Percentage Error (MAPE) of peak load by 3% to 10%.

Otoacoustic emissions (OAEs) are low-level sounds generated by the cochlea and widely used as a noninvasive tool to inspect cochlear impairments. However, only the amplitude information of OAE signals is used in current clinical tests, while the OAE phase containing important information about cochlear functions is commonly discarded, due to the insufficient frequency-resolution of existing OAE tests. In this study, swept tones with time-varying frequencies were used to measure stimulus frequency OAEs (SFOAEs) in human subjects, so that high-resolution phase spectra that are not available in existing OAE tests could be obtained and analyzed. The results showed that the phase of swept-tone SFOAEs demonstrated steep gradients as the frequency increased in human subjects with normal hearing. The steep phase gradients were sensitive to auditory functional abnormality caused by cochlear damage and stimulus artifacts introduced by system distortions. At low stimulus levels, the group delays derived from the phase gradients decreased from around 8.5 to 3 ms as the frequency increased from 1 to 10 kHz for subjects with normal hearing, and the pattern of group-delay versus frequency function showed significant difference for subjects with hearing loss. By using the swept-tone technology, the study suggests that the OAE phase gradients could provide highly sensitive information about the cochlear functions and therefore should be integrated into the conventional methods to improve the reliability of auditory health screening.

Electrocardiogram (ECG) is a critical physiological indicator that contains abundant information about human heart activities. However, it is a kind of weak low-frequency signal, which is easy to be interfered by various noises. Therefore, wearable biosensors (WBS) technique is introduced to overcome this challenge. A flexible non-contact electrode is proposed for wearable biosensors (WBS) system, which is made up of flexible printed circuits materials, and can monitor the ECG signals during exercise for a long time. It uses the principle of capacitive coupling to obtain high-quality signals, and reduces the impact of external noise through active shielding; The results showed that the proposed non-contact electrode was equivalent to a medical wet electrode. The correlation coefficient was as high as 99.70 ± 0.30% when the subject was resting, while it was as high as 97.53 ± 1.80% during exercise. High-quality ECG could still be collected at subjects walking at 7 km/h. This study suggested that the proposed flexible non-contact electrode would be a potential tool for wearable biosensors for medical application on long-term monitoring of patients’ health and provide athletes with physiological signal measurements.

High-precision extraction of particulate characteristic modes is essential for dust explosion safety measurements, such as particulate concentration and size distribution. A new solution based on information entropy multi-decision attribute reduction fuzzy rough set is proposed to analyse the particulate morphology characteristics, which effectively avoids the shortcomings of traditional technology (low accuracy, stochasticity, etc.). The proposed approach consists of three stages: membership function modelling, attribute reduction, and maximum information entropy threshold segmentation. The membership coefficient was determined with a multi-segment function by developing the fuzzy degree of the membership model for dust image pixels. The fuzzy dependence of the conditional attribute was determined to extract the fuzzy attribute reduction. Finally, the model of coal dust particulates with information entropy was improved to extract the maximum segmentation threshold, which is significant for classification. The proposed methods were evaluated over a sequence of 30 image sets. The unclassified rate evaluation reached 0.978 for particle sizes ≥ 200 μm, 0.958 for [75 μm, 200 μm] and 0.950 for particle sizes

The dilated convolution algorithm, which is widely used for image segmentation, is applied in the image classification field in this paper. In many traditional image classification algorithms, convolution neural network (CNN) plays an important role. However, the classical CNN has the problem of consuming too much computing resources. To solve this problem, first, this paper proposed a dilated CNN model which is built through replacing the convolution kernels of traditional CNN by the dilated convolution kernels, and then, the dilated CNN model is tested on the Mnist handwritten digital recognition data set. Second, to solve the detail loss problem in the dilated CNN model, the hybrid dilated CNN (HDC) is built by stacking dilated convolution kernels with different dilation rates, and then the HDC model is tested on the wide-band remote sensing image data set of earth's terrain. The results show that under the same environment, compared with the traditional CNN model, the dilated CNN model reduces the training time by 12.99% and improves the training accuracy by 2.86% averagely, compared with the dilated CNN model, the HDC model reduces the training time by 2.02% and improves the training and testing accuracy by 14.15% and 15.35% averagely. Therefore, the dilated CNN and HDC model proposed in this paper can significantly improve the image classification performance.

Brain-machine interfaces (BMIs) are broadly defined as systems that establish direct communications between living brain tissue and external devices, such as artificial arms. By sensing and interpreting neuronal activities to actuate an external device, BMI-based neuroprostheses hold great promise in rehabilitating motor disabled subjects, such as amputees. In this paper, we develop a control-theoretic analysis of a BMI-based neuroprosthetic system for voluntary single joint reaching task in the absence of visual feedback. Using synthetic data obtained through the simulation of an experimentally validated psycho-physiological cortical circuit model, both the Wiener filter and the Kalman filter based linear decoders are developed. We analyze the performance of both decoders in the presence and in the absence of natural proprioceptive feedback information. By performing simulations, we show that the performance of both decoders degrades significantly in the absence of the natural proprioception. To recover the performance of these decoders, we propose two problems, namely tracking the desired position trajectory and tracking the firing rate trajectory of neurons which encode the proprioception, in the model predictive control framework to design optimal artificial sensory feedback. Our results indicate that while the position trajectory based design can only recover the position and velocity trajectories, the firing rate trajectory based design can recover the performance of the motor task along with the recovery of firing rates in other cortical regions. Finally, we extend our design by incorporating a network of spiking neurons and designing artificial sensory feedback in the form of a charged balanced biphasic stimulating current.

The clinical need for effective bone regeneration in compromised conditions continues to drive demand for innovative solutions. Among emerging strategies, extracellular vesicles (EVs) have shown promise as an acellular approach for bone regeneration. However, their efficacy is hindered by rapid sequestration and clearance when administered via bolus injection. To address this challenge, EV-functionalized scaffolds have recently been proposed as an alternative delivery strategy to enhance EV retention and subsequent healing efficacy. This review aims to consolidate recent advancements in the development of EV-functionalized scaffolds for augmenting bone regeneration. It explores various sources of EVs and different strategies for integrating them into biomaterials. Furthermore, the mechanisms underlying their therapeutic effects in bone regeneration are elucidated. Current limitations in clinical translation and perspectives on the design of more efficient EVs for improved therapeutic efficacy are also presented. Overall, this review can provide inspiration for the development of novel EV-assisted grafts with superior bone regeneration potential. [ABSTRACT FROM AUTHOR]