The role of electrocochleography and the caloric test in predicting short-term recurrence of benign paroxysmal positional vertigo

ObjectiveThe study aimed to assess the value of physiological tests for evaluating inner ear function in predicting the short-term recurrence of benign paroxysmal positional vertigo (BPPV).Materials and methodsThe clinical information of all idiopathic BPPV patients who were treated in our clinic between February 2021 and December 2022 were reviewed. All patients included in the study had completed audiology examinations including pure tone audiometry, electrocochleography (EcochG), auditory brainstem response, and vestibular function examination such as the vestibular caloric test. The relationships between the results of the above tests and short-term recurrent BPPV were analyzed.ResultsA total of 96 patients with unilateral idiopathic BPPV were included for analysis. The numbers of non-recurrent patients and recurrent patients were 57 (59.4%) and 39 (40.6%), respectively. Only the results of EcochG and the caloric test showed significant differences between non-recurrent and recurrent patients (both P < 0.05). The results of these two tests were also found to be independently predictive of short-term recurrence (both P < 0.05). The non-recurrence rate for patients with normal results in both tests reached up to 78.3%, which was significantly higher than that for patients with abnormal results in both tests, 28.6% (P < 0.05).ConclusionEndolymphatic hydrops and canal paresis were independent risk factors for short-term recurrent BPPV. Additional treatments should be considered to reduce the recurrence rate, including dehydration treatment and vestibular rehabilitation

Review and prospect of coal rock hydraulic fracturing physical experimental research

Physical simulation of hydraulic fracturing is an approximate representation of fracture evolution and its dynamic process, which represents an important direction of fracture evolution research. Similarity theory is the theoretical basis of the transformation between field prototype and experimental model. Test equipment and similar materials are the material premise of physical simulation experiment. Monitoring and detection technology is the key part to evaluate the fracturing effect of hydraulic fracturing. This paper summarizes the development of similarity theory of hydraulic fracturing physical experiments, the evolution of experimental materials and devices, and the characteristics and application scope of common monitoring and detection methods from the above three aspects. The analysis shows that: the similarity criterion of hydraulic fracturing has been preliminarily formed, but it needs to be further modified according to the physical and mechanical properties of coal and rock. Numerical simulation method can be used to explore the influence degree of minor factors ignored in the derivation of similarity criterion, so as to improve the reliability and applicability of the empirical equation. In view of various physical and mechanical properties of coal and rock, many empirical formula equations of similar materials have been obtained at present, but a set of detailed experimental specification and a large number of experimental attempts are still needed to improve the repeatability of the experiments, so as to establish a more universal database of empirical equations of similar material matching. Fracturing devices are developing towards the direction of multi-field coupling with more simulation conditions, larger simulation scale and wider simulation range, and fracturing methods are gradually diversified with engineering applications. However, the accuracy of triaxial loading of fracturing devices needs to be further improved to ensure effective fracturing experiments under high stress conditions, and reduce the impact of experimental operations on the final results. Monitoring methods and detection technologies have their own advantages in evaluating the fracturing effect of hydraulic fracturing, and similar materials also have a significant impact on the effectiveness and accuracy of monitoring methods and detection technologies. How to rationally select and combine monitoring methods and detection technologies based on experiments is the key to meet the research needs of micro-structures

Rapid Diagnosis of HIV-1 virus by Near Infrared Spectroscopy: based on Partial least squares regression

Currently, the laboratory diagnostic tests available for HIV-1 viral infection are mainly based on serological testing which relies on enzyme-linked immunosorbent assay (ELISA) for blood HIV antigen detection and reverse transcription polymerase chain reaction (RT-PCR) for HIV specific RNA sequence identification. However, these methods are expensive and time-consuming, and suffer from false positive and/or false negative results. Thus, there is an urgent need for developing a cost effective, rapid and accurate diagnostic method for HIV-1 infection. In order to reduce the barriers for effective diagnosis, a near-infrared spectroscopy (NIR) method was used to detect the HIV-1 virus in human serum, specifically, three absorption peaks with dose-dependent at 1582nm, 1810nm and 2363nm were found by multiple FBiPLSR test analysis for HIV-nano and HIV-EGFP, but not for MLV. Therefore, we recommend the use of 1582nm, 1810nm and 2363nm as the characteristic spectrum peak, for early screening and rapid diagnosis of serum HIV

A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation

Cross-domain recommendation (CDR) aims to enhance recommendation accuracy in a target domain with sparse data by leveraging rich information in a source domain, thereby addressing the data-sparsity problem. Some existing CDR methods highlight the advantages of extracting domain-common and domain-specific features to learn comprehensive user and item representations. However, these methods can't effectively disentangle these components as they often rely on simple user-item historical interaction information (such as ratings, clicks, and browsing), neglecting the rich multi-modal features. Additionally, they don't protect user-sensitive data from potential leakage during knowledge transfer between domains. To address these challenges, we propose a Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation, called P2M2-CDR. Specifically, we first design a multi-modal disentangled encoder that utilizes multi-modal information to disentangle more informative domain-common and domain-specific embeddings. Furthermore, we introduce a privacy-preserving decoder to mitigate user privacy leakage during knowledge transfer. Local differential privacy (LDP) is utilized to obfuscate the disentangled embeddings before inter-domain exchange, thereby enhancing privacy protection. To ensure both consistency and differentiation among these obfuscated disentangled embeddings, we incorporate contrastive learning-based domain-inter and domain-intra losses. Extensive Experiments conducted on four real-world datasets demonstrate that P2M2-CDR outperforms other state-of-the-art single-domain and cross-domain baselines

Causal Disentanglement for Regulating Social Influence Bias in Social Recommendation

Social recommendation systems face the problem of social influence bias, which can lead to an overemphasis on recommending items that friends have interacted with. Addressing this problem is crucial, and existing methods often rely on techniques such as weight adjustment or leveraging unbiased data to eliminate this bias. However, we argue that not all biases are detrimental, i.e., some items recommended by friends may align with the user's interests. Blindly eliminating such biases could undermine these positive effects, potentially diminishing recommendation accuracy. In this paper, we propose a Causal Disentanglement-based framework for Regulating Social influence Bias in social recommendation, named CDRSB, to improve recommendation performance. From the perspective of causal inference, we find that the user social network could be regarded as a confounder between the user and item embeddings (treatment) and ratings (outcome). Due to the presence of this social network confounder, two paths exist from user and item embeddings to ratings: a non-causal social influence path and a causal interest path. Building upon this insight, we propose a disentangled encoder that focuses on disentangling user and item embeddings into interest and social influence embeddings. Mutual information-based objectives are designed to enhance the distinctiveness of these disentangled embeddings, eliminating redundant information. Additionally, a regulatory decoder that employs a weight calculation module to dynamically learn the weights of social influence embeddings for effectively regulating social influence bias has been designed. Experimental results on four large-scale real-world datasets Ciao, Epinions, Dianping, and Douban book demonstrate the effectiveness of CDRSB compared to state-of-the-art baselines

The Farmers’ Channel Selection and Sustainable Analysis under Carbon Tax Policy

This paper examines a farmer’s channel selection in a supply chain led by a retailer, considering carbon emissions and products’ deterioration. Three channels—online channels, retail channels, and dual channels—are proposed. The inventory model of perishable products and the two-stage Stackelberg game model are used to illustrate the operational process. To compare performances of the three channel structures, we further determine the critical points consisting of the profits and the carbon emissions among these channels. The results provide useful insights for supply chain members and the government. Farmers can choose a channel to optimize profit with respect to deterioration rate and product yield, but it might conflict with the aim of least carbon emissions. When the deterioration rate is high, the online channel is not a suitable choice. For the government, the carbon tax contributes to the reduction of carbon emissions, but it also leads to the loss of the farmer’s profit. Additionally, numerical results further illustrate that, from the perspective of the government, transporting and inventory processes are two major sources of emissions, and it is essential to implement carbon tax and exploit low-carbon transportation

Acupotomy Treatment for Lumbar Disc Herniation

Background The aim of this study was to examine whether the effects of acupotomy therapy were beneficial for the treatment of protrusion of lumbar intervertebral disc. Methods The number of patients (n = 80) were equally assigned into treatment group and control group. Treatment group was given acupotomy therapy twice a week, and control group was given acupuncture 3 times a week, for 4 weeks. The beneficial effect and changes in score of the Japan Orthopedic Association (JOA) for low back pain were observed. Results Among 40 cases in the treatment group, there were 25 (62.5%) with an excellent effect, 13 (32.5%) with good effect, 1 (2.5%) with a medium effect and 1 (2.5%) with poor effect, with the total experiencing an excellent/good effect of 95.0%. Among 40 cases in the control group, there were 11 (27.5%) with an excellent effect, 17 (42.5%) with good effect, 10 (25.0%) with a medium effect, and 2 (5.0%) with poor effect, with an excellent/good rate of 70.0%. The result of the rank sum test showed Z = −4.923, p < 0.05 in the comparison, indicating a significantly better outcome following acupotomy compared with acupuncture. JOA scores increased in both groups after treatment (p < 0.05), which was more significant in the acupotomy treatment group (p < 0.05). Conclusion Acupotomy therapy has a beneficial effect on protrusion of lumbar intervertebral disc

Investigation of the Ultrasonic Stimulation Performance on Permeability Enhancement in Coal Seam: Field Tests and In Situ Permeability Evaluation

Power ultrasonic-assisted reservoir modification is a promising technique for enhanced coalbed methane recovery. However, the in-site performance of ultrasound-assisted CBM production has not yet been revealed. In the current study, the in situ antireflection test was conducted with high-power ultrasound ~18 kW in underground coal seam, and the antireflection performance was investigated by measuring the borehole drainage gas data in the field test zone, and then, the in situ permeability change of the target coal seam was evaluated numerically. The result shows that, within 40 days’ drainage after ultrasonic antireflection in coal seam, the average gas concentration of single borehole in the experimental group increased by 81.4 %~227.3% than that in control group, the average borehole gas flowrate has a 20%~106% improvement over the control group, and the pure methane production in single borehole increased by about 3.83 times. The permeability inversion indicates that the in situ coal seam permeability has increased by at least 2.36 times after the ultrasound stimulation within the range of 8 m from the ultrasound source