Research on method of vibration analysis of rubber tracked vehicle based on dynamic model

To understand the vibration characteristics of rubber track system in traveling, this research studied the small harvester installed with rubber track system and the dynamic model reflecting vibration characteristics of rubber track system on the ground was constructed. Comparing analysis results with measured experimental data obtained from vehicle test, it is proved that the dynamic model established by theoretical analysis can correctly and effectively predict actual movement condition and vibration characteristics of rubber track system, especially at low test vehicle speeds. The relative difference between measured data of vibration acceleration obtained from real vehicle tests and the theoretical value was in the range of –1.2 %-+18.2 %. The vibration prediction and analysis method of rubber tracked vehicle was discussed in this study, and important basic data were provided for the research of comfort evaluation of working posture and lightweight design of rubber tracked mechanism

Diagnostic value and integrated threshold of ESR for diabetic foot osteomyelitis: a systemic review and meta-analysis

BackgroundDiabetic foot osteomyelitis (DFO) is a severe complication of diabetic foot infections (DFI). Early and accurate diagnosis is crucial for improving patient outcomes. The erythrocyte sedimentation rate (ESR), a commonly used inflammatory marker, has been controversial regarding its diagnostic value and optimal cutoff values in DFO.ObjectiveThis study aims to conduct a systematic review and meta-analysis to comprehensively evaluate the diagnostic efficacy of ESR for DFO and to determine an optimal pooled cutoff value for the marker.MethodsA systematic search of PubMed, EMBASE, Cochrane Library, OVID, and the Wanfang database was conducted for literature related to ESR in the diagnosis of DFO, with the search period extending through March 2025. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied for quality evaluation of the included studies. Statistical analyses were performed using Stata 18.0 to generate hierarchical summary receiver operating characteristic (HSROC) curves and forest plots for assessing the diagnostic performance of ESR in DFO. To determine the optimal composite cutoff value of ESR for diagnosing DFO, a different random intercepts and common random slope (DICS) model was implemented using R4.5.0. Subsequently, a Generalized Linear Mixed Model (GLMM) was constructed to predict the corresponding sensitivity and specificity at the ESR threshold of 70 mm/h.ResultsFollowing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 12 studies with a total of 1,674 subjects were included. The HSROC model revealed that the area under the curve (AUC) for ESR in diagnosing DFO was 0.71, with sensitivity and specificity values of 0.76 and 0.73, respectively. The DICS model identified an optimal pooled cutoff value for ESR at 51.6 mm/h, with corresponding sensitivity and specificity values of 0.80 and 0.67, respectively. Using the GLM model, an ESR cutoff of 70 mm/h yielded sensitivity and specificity of 0.61 and 0.83, respectively.ConclusionESR demonstrates moderate diagnostic efficacy in the identification of DFO. Based on our findings, we recommend the optimal pooled cutoff value for ESR is 51.6 mm/h, as a preliminary screening tool in the diagnostic workup of DFO

Implicit Factorization with Shared Any Bits

At PKC 2009, May and Ritzenhofen proposed the implicit factorization problem (IFP). They showed that it is undemanding to factor two h-bit RSA moduli N1=p1q1, N2=p2q2 where q1, q2 are both αh-bit, and p1, p2 share uh&gt;2αh the least significant bits (LSBs). Subsequent works mainly focused on extending the IFP to the cases where p1, p2 share some of the most significant bits (MSBs) or the middle bits (MBs). In this paper, we propose a novel generalized IFP where p1 and p2 share an arbitrary number of bit blocks, with each block having a consistent displacement in its position between p1 and p2, and we solve it successfully based on Coppersmith’s method. Specifically, we generate a new set of shift polynomials to construct the lattice and optimize the structure of the lattice by introducing a new variable z=p1. We derive that we can factor the two moduli in polynomial time when u&gt;2(n+1)α(1−α^1/(n+1)) with p1, p2 sharing n blocks. Further, no matter how many blocks are shared, we can theoretically factor the two moduli as long as u&gt;2αln(1/α). In addition, we consider two other cases where the positions of the shared blocks are arbitrary or there are k&gt;2 known moduli. Meanwhile, we provide the corresponding solutions for the two cases. Our work is verified by experiments. </p

AV-TranSpeech: Audio-Visual Robust Speech-to-Speech Translation

Direct speech-to-speech translation (S2ST) aims to convert speech from one language into another, and has demonstrated significant progress to date. Despite the recent success, current S2ST models still suffer from distinct degradation in noisy environments and fail to translate visual speech (i.e., the movement of lips and teeth). In this work, we present AV-TranSpeech, the first audio-visual speech-to-speech (AV-S2ST) translation model without relying on intermediate text. AV-TranSpeech complements the audio stream with visual information to promote system robustness and opens up a host of practical applications: dictation or dubbing archival films. To mitigate the data scarcity with limited parallel AV-S2ST data, we 1) explore self-supervised pre-training with unlabeled audio-visual data to learn contextual representation, and 2) introduce cross-modal distillation with S2ST models trained on the audio-only corpus to further reduce the requirements of visual data. Experimental results on two language pairs demonstrate that AV-TranSpeech outperforms audio-only models under all settings regardless of the type of noise. With low-resource audio-visual data (10h, 30h), cross-modal distillation yields an improvement of 7.6 BLEU on average compared with baselines. Audio samples are available at https://AV-TranSpeech.github.ioComment: Accepted to ACL 202

Mega-TTS 2: Zero-Shot Text-to-Speech with Arbitrary Length Speech Prompts

Zero-shot text-to-speech aims at synthesizing voices with unseen speech prompts. Previous large-scale multispeaker TTS models have successfully achieved this goal with an enrolled recording within 10 seconds. However, most of them are designed to utilize only short speech prompts. The limited information in short speech prompts significantly hinders the performance of fine-grained identity imitation. In this paper, we introduce Mega-TTS 2, a generic zero-shot multispeaker TTS model that is capable of synthesizing speech for unseen speakers with arbitrary-length prompts. Specifically, we 1) design a multi-reference timbre encoder to extract timbre information from multiple reference speeches; 2) and train a prosody language model with arbitrary-length speech prompts; With these designs, our model is suitable for prompts of different lengths, which extends the upper bound of speech quality for zero-shot text-to-speech. Besides arbitrary-length prompts, we introduce arbitrary-source prompts, which leverages the probabilities derived from multiple P-LLM outputs to produce expressive and controlled prosody. Furthermore, we propose a phoneme-level auto-regressive duration model to introduce in-context learning capabilities to duration modeling. Experiments demonstrate that our method could not only synthesize identity-preserving speech with a short prompt of an unseen speaker but also achieve improved performance with longer speech prompts. Audio samples can be found in https://mega-tts.github.io/mega2_demo/

Exploration and application of deep learning based wellbore deformation forecasting model

In recent years, a number of vertical shaft tilt deformation and breakage disasters have occurred in the eastern mining areas of China, which have seriously affected mine safety and production. In response to the tilting and damage disasters of deep vertical shafts in thick water-bearing loose layers, the tilting and deformation monitoring of shafts was carried out by taking the deep vertical shaft (800 m) of a mine in Lunan as the research object, studying the spatial and temporal change characteristics of shaft tilting, and analyzing the main influencing factors of shaft tilting; based on this, based on the deep learning theory, four types of deep learning method, namely, recurrent neural network (RNN), long and short-term memory network (LSTM), gated recurrent unit (GRU), and one-dimensional convolutional neural network (1DCNN), were used. unit (GRU), and one-dimensional convolutional neural network (1DCNN) to construct a wellbore tilt deformation prediction model, and compare the prediction results with the measured values to analyze the accuracy of the wellbore deformation prediction model, validate the reliability of the model, studied overall wellbore and critical area prediction effects, and carry out engineering applications. The study shows that: ① The wellbore tilt mainly occurs in the loose layer, the tilt value decreases linearly from shallow to deep, and is biased towards the side of the extraction zone, with a maximum of 352 mm, and the deformation of the bedrock layer is smaller, with a maximum of 88 mm; the increase in the range of deformation propagation in the thick loose layer caused by the mining, and the change of seepage hydrophobicity of the aquifer at the bottom along the wall of the well and the seepage field of the groundwater are the main causes of the tilted deformation of the wellbore. ② The Spearman correlation coefficient between the model and the measured value is 0.978 at the maximum and 0.867 at the minimum;the maximum difference between the four models and the field measured offsets is 0.043 m, the mean absolute error EMA is within 0.003–0.009 m, and the root mean square error ERMS is within 0.004–0.011 m. The overall prediction is optimized by the 1DCNN model, and the main tilting direction (The prediction accuracy of the main inclined direction (east-west direction, which is inclined to the side of the mining area) is slightly lower than that of the direction with smaller deformation amount (north-south direction), and all of them can meet the engineering needs. ③ The overall prediction curve of the wellbore is consistent with the actual tilt direction, and the average values of EMA and ERMS of the wellhead and loose bedrock interface are 0.005 m and 0.006 m. The accuracy of the wellbore bottoming is a little bit lower, with the corresponding values of 0.012 m and 0.013 m. The wellbore characteristic area and overall prediction effect are good, indicating that the wellbore deformation prediction model based on deep learning has good prediction ability. The research results have been effectively applied in the wellbore grouting repair and management project, which provides technical reference and data support for the safe management of wellbore, and provides engineering practical experience for similar projects

Surface deformation law of mining under thick loose layer and thin bedrock: taking the southern Shandong Mining Area as an example

The surface subsidence in the thick loose layer and thin bedrock mining area in the east of China has the characteristics of large subsidence value, wide movement range and long settling time. Taking a coal mine in Southern Shandong Mining Area as an example,this paper discusses the variation rules of coal seam mining surface deformation parameters under different loose layer and bedrock thickness ratio conditions, on the basis of field measurements, using FLAC3D, and establishes a surface deformation calculation model for coal seam mining under the conditions of different loose layer bedrock thickness ratios (0.25−5.00), studies the characteristics of surface deformation, analyzed the influence of ratio of loose layer thickness to bedrock thickness on the parameters of probability integral method, and quantitatively analyzed and discussed the conditions of thick loose layer and thin bedrock from the perspective of mining subsidence. Research shows: ①Under the same mining thickness conditions,when the ratio of loose layer thickness to bedrock thickness increases, the surface deformation amount obviously increases first and then decrease, when the ratio reaches a certain limit, the ground surface deformation tends to be stabilized; ②The subsidence coefficient, the horizontal movement coefficient and the tangent of the main influence angle all increase first and then decreases, and the inflection point is 1.75,1.25 and 1.25, respectively; ③The proportion of loose bed thickness in the average mining depth has great influence on the angle of draw and boundary angle. The boundary angle and the angle of draw gradually decrease with the increase of the ratio. Based on the above research, it is proposed that the ratio of 1.25−1.75 is the critical value for the condition of thick loose bedding and thin bedrock, which provides a theoretical basis and technical reference for the prediction of surface deformation and the prevention and control of mining subsidence disasters in typical thick loose layer thin bedrock mining areas in eastern China

Eucommia ulmoides seed oil is a complementary food for suppressing digestive tumors

BackgroundNatural products and their bioactive components serve as valuable resources for anticancer drug discovery. Eucommia ulmoides, a medicinal and edible plant widely used in traditional medicine, contains functionally significant compounds in its seeds, particularly Eucommia ulmoides seed oil (EUSO). Previous studies have demonstrated EUSO’s promising preventive and therapeutic potential against metabolic disorders, including hypertension, diabetes, and obesity. However, its therapeutic effects on malignancies, particularly digestive system cancers, remain unexplored.MethodsTo evaluate the antitumor effects of EUSO, we performed in vitro and in vivo functional analyses using Cell viability, clone formation, migration capacities, and apoptosis rates were assessed through CCK-8 assays, colony formation assays, Transwell assays, and flow cytometry in hepatocellular carcinoma (HCC) and pancreatic cancer cell models. In vivo antitumor efficacy was further validated using subcutaneous xenograft models in nude mice. Mechanistically, transcriptomic profiling (RNA-seq) and Western blotting were conducted to identify EUSO-regulated signaling pathways.ResultsEUSO exhibited dose-dependent suppression of HCC and pancreatic cancer cell proliferation, colony formation, and migration. Flow cytometry confirmed EUSO-induced apoptosis. In vivo, EUSO administration suppressed tumor growth in xenograft models. Mechanistic studies revealed that EUSO downregulated PI3K-AKT-mTOR pathway activation, evidenced by reduced phosphorylation of AKT (Ser473) and mTOR (Ser2448).ConclusionEUSO attenuates the malignant progression of digestive system cancers by inhibiting the PI3K-AKT-mTOR pathway. These results provide mechanistic evidence supporting the potential application of EUSO as an adjuvant therapeutic agent in cancer management and warrant further clinical investigation into its chemopreventive and complementary therapeutic value

Progress of fluorescence imaging in lymph node dissection surgery for prostate and bladder cancer

Fluorescence imaging is a relatively new imaging method used to visualize different tissue structures to help guide intraoperative operations, which has potential advantages with high sensitivity and contrast compared to conventional imaging. In this work, we review fluorescent contrast agents and devices used for lymphatic system imaging. Indocyanine green is the most widely utilized due to its high sensitivity, specificity, low background fluorescence, and safety profile. In prostate and bladder cancer lymph node dissection, the complex lymphatic drainage can result in missed metastatic nodes and extensive dissection increases the risk of complications like lymphocele, presenting a significant challenge for urologists. Fluorescence-guided sentinel lymph node dissection facilitates precise tumor staging. The combination of fluorescence and radiographic imaging improves the accuracy of lymph node staging. Multimodal imaging presents new potential for precisely identifying metastatic pelvic lymph nodes