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Background: Locomotive syndrome (LS) is characterized by reduced mobility. Clinical decision limit (CDL) stage 3 in LS indicates physical frailty. Lumbar spinal canal stenosis (LSS) is one of the causes of LS, for which lumbar surgery is considered to improve the CDL stage. This study aimed to investigate the efficacy of lumbar surgery and independent factors for improving the CDL stage in patients with LSS. Methods: This retrospective study was conducted at the Department of Orthopaedic Surgery at our University Hospital. A total of 157 patients aged ≥ 65 years with LSS underwent lumbar surgery. The 25-Question Geriatric Locomotive Function scale (GLFS-25) was used to test for LS, and the Timed Up and Go test (TUG) was used to evaluate functional ability. Lower limb pain was evaluated using a visual analog scale. Patients with at least one improvement in the CDL stage following lumbar surgery were included in the improvement group. Differences in lower limb pain intensity between the groups were evaluated using the Wilcoxon rank-sum test. The Spearman's rank correlation coefficient was used to determine correlations between Δ lower limb pain and Δ GLFS-25. Logistic regression analysis was used to identify factors associated with improvement in LS. Results: The proportion of patients with improved CDL stage was 45.1% (improvement/non-improvement: 32/39). Δ Lower limb pain was significantly reduced in the improvement group compared with that in the non-improvement group (51.0 [36.3–71.0] vs 40.0 [4.0–53.5]; p = 0.0107). Δ GLFS-25 was significantly correlated with Δ lower limb pain (r = 0.3774, p = 0.0031). Multiple logistic regression analysis revealed that TUG and age were significantly associated with improvement in LS (odds ratio, 1.22; 95% confidence interval: 1.07–1.47). Conclusions: Lumbar surgery effectively improved the CDL stage in patients with LSS. In addition, TUG was an independent factor associated with improvement in the CDL. [ABSTRACT FROM AUTHOR]

Ionospheric sporadic E layers (Es) are intense plasma irregularities between 80 and 130 km in altitude and are generally unpredictable. Reconstructing the morphology of sporadic E layers is not only essential for understanding the nature of ionospheric irregularities and many other atmospheric coupling systems, but is also useful for solving a broad range of demands for reliable radio communication of many sectors reliant on ionosphere-dependent decision-making. Despite the efforts of many empirical and theoretical models, a predictive algorithm with both high accuracy and high efficiency is still lacking. Here we introduce a new approach for Sporadic E Layer Forecast using Artificial Neural Networks (SELF-ANN). The prediction engine is trained by fusing observational data from multiple sources, including a high-resolution ERA5 reanalysis dataset, Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultation (RO) measurements, and integrated data from OMNIWeb. The results show that the model can effectively reconstruct the morphology of the ionospheric E layer with intraseasonal variability by learning complex patterns. The model obtains good performance and generalization capability by applying multiple evaluation criteria. The random forest algorithm used for preliminary processing shows that local time, altitude, longitude, and latitude are significantly essential for forecasting the E-layer region. Extensive evaluations based on ground-based observations demonstrate the superior utility of the model in dealing with unknown information. The presented framework will help us better understand the nature of the ionospheric irregularities, which is a fundamental challenge in upper-atmospheric and ionospheric physics. Moreover, the proposed SELF-ANN can make a significant contribution to the development of the prediction of ionospheric irregularities in the E layer, particularly when the formation mechanisms and evolution processes of the Es layer are not well understood. [ABSTRACT FROM AUTHOR]

The optimization of locomotive drivers' scheduling in rail freight transportation comes as a necessity for minimizing economic expenses and training investments. The Ferodata AI engine, an artificial intelligence (AI)/machine learning (ML) software module, developed by our team, has integrated a supervised random forest model that automatically assigns conductors to freight transportation orders based on the data about locomotive driver's tiredness score, distance of the driver to the departure point of a transportation order, driver availability, and circulation history. The model proposed by us obtained very good performance metrics on the train set (accuracy: 95%, AUC: 0.9905) and reasonably good and encouraging performance on the test set (accuracy: 84%, AUC: 0.8357). After rigorous testing and validation on external and larger datasets, the automated optimization of locomotive driver assignments could bring operational efficiency, cost savings, regulatory compliance, and improved safety to scheduled rail freight transports. [ABSTRACT FROM AUTHOR]

Cybersecurity is a critical issue in today's internet world. Classical security systems, such as firewalls based on signature detection, cannot detect today's sophisticated zero-day attacks. Machine learning (ML) based solutions are more attractive for their capabilities of detecting anomaly traffic from benign traffic, but to develop an ML-based anomaly detection system, we need meaningful or realistic network datasets to train the detection engine. There are many public network datasets for ML applications. Still, they have limitations, such as the data creation process and the lack of diverse attack scenarios or background traffic. To create a good detection engine, we need a realistic dataset with various attack scenarios and various types of background traffic, such as HTTPs, streaming, and SMTP traffic. In this work, we have developed realistic network data or datasets considering various attack scenarios and diverse background/benign traffic. Furthermore, considering the importance of distributed denial of service (DDoS) attacks, we have compared the performance of detecting anomaly traffic of some classical supervised and our prior developed unsupervised ML algorithms based on the convolutional neural network (CNN) and pseudo auto-encoder (AE) architecture based on the created datasets. The results show that the performance of the CNN-Pseudo-AE is comparable to that of many classical supervised algorithms. Hence, the CNN-Pseudo-AE algorithm is promising in actual implementation. [ABSTRACT FROM AUTHOR]

With the continuous iteration and update of railway signal systems, products, and functions, the handling of abnormal locomotive signal information in the daily maintenance of signal equipment becomes particularly complex and cumbersome. This article proposes the use of joint data analysis methods and measures to accurately locate faults in abnormal locomotive signal information through on-site cases of abnormal locomotive signals, in order to provide assistance and experience accumulation for on-site electrical maintenance personnel, and ensure the safe and stable operation of signal equipment. [ABSTRACT FROM AUTHOR]

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This article concerns two vocal interpretations of the poem Dwa wiatry (Two Winds) by Julian Tuwim: one comes from an actor, the other from an amateur publishing educational materials on the Internet. The aim is to explain why the actor's interpretation seems more suggestive than the amateur one. The subject of the study is the prosodic features of both performances of the poem (tempo, sound intensity, and tone changes), treated as an iconic indicator of its cognitive and emotional content. This article is a continuation of an earlier publication in which Tuwim's Lokomotywa (Locomotive) was examined in a similar way. In both studies, the Praat programme for acoustic analysis of sound was used for research. [ABSTRACT FROM AUTHOR]