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Predictable recovery rates in near-surface materials after earthquake damage
Abstract Earthquakes introduce transient mechanical damage in the subsurface, which causes postseismic hazards and can take years to recover. This observation has been linked to relaxation, a phenomenon observed in a wide range of materials after straining perturbations, but systematic controls on the recovery duration in the shallow subsurface after earthquake ground shaking have not been determined. Here, we analyse the effects of two successive large earthquakes and their aftershocks on ground properties using estimates of seismic velocity from ambient noise interferometry. We show that the relaxation time scale is a constant that is an intrinsic property of the substrate, independent of the intensity of ground shaking. Our study highlights the predictability of earthquake damage dynamics in the shallow subsurface and also in other materials. This finding may be reconciled with existing state variable frameworks by considering the superposition of different populations of damaged contacts.
A certificateless aggregate signature scheme for VANETs with privacy protection properties
Aggregate signatures are excellent in simultaneously verifying the validity of multiple signatures, which renders them highly suitable for bandwidth-constrained environments. The certificateless public key system is among the most advanced public key cryptosystems at present. Scholars have combined their advantages to develop certificateless aggregate signature schemes, which are applicable to the secure communication of Vehicular Ad-hoc Networks (VANETs). Recently, Cahyadi E F et al. put forward a certificateless aggregate signature scheme specifically designed for use in VANETs. Regrettably, through our strict security analysis, we discovered at least two major vulnerabilities in the signature scheme: a public key replacement attack and a malicious KGC (Key Generation Center) attack. To tackle these vulnerabilities, our article not only presents the methods of these attacks but also explores the fundamental reasons for their feasibility. Additionally, we propose specific improvement measures and show that the enhanced scheme retains its security under the random oracle model. The stability of the improved scheme depends on the computational complexity of the Diffie-Hellman problem. Finally, a comprehensive assessment involving security, computational cost, communicational cost, and calculational efficiency overhead highlights the excellent performance of our proposed solution.
Interaction between waves and vegetation
DeepMind AI crushes tough maths problems on par with top human solvers
Actin-dependent α-catenin oligomerization contributes to adherens junction assembly
Speech emotion recognition using fine-tuned Wav2vec2.0 and neural controlled differential equations classifier
Speech emotion recognition (SER) has always been a popular yet challenging task with broad applications in areas such as social media communication and medical diagnostics. Due to the characteristics of speech emotion recognition dataset, which often have small data volumes and high complexity, effectively integrating and modeling audio data remains a significant challenge in this field. To address this, we propose a model architecture that combines fine-tuned Wave2vec2.0 with Neural Controlled Differential Equations (NCDEs): First, we use a fine-tuned Wav2vec2.0 to extract rich contextual information. Then we model the high-dimensional time series feature set using a Neural Controlled Differential Equation classifier. We set the vector field as an MLP and update the model’s hidden state by solving the controlled differential equation. We conducted speech emotion recognition experiments on the IEMOCAP dataset. The experiments show that our model achieves the weighted accuracy of 73.37% and the unweighted accuracy of 74.18%. Additionally, our model converges very quickly, reaching a good accuracy after just one epoch of training. Furthermore, our model exhibits excellent stability. The standard deviation of weighted accuracy (WA) is 0.45% and the standard deviation of unweighted accuracy (UA) is 0.39%.
MRI-based analysis of thigh intramuscular fat and its associations with age, sex, and BMI using data from the osteoarthritis initiative data
Abstract The degree of thigh intramuscular fat in individuals without OA is fundamental for distinguishing natural variations in intramuscular fat from pathological changes. The goals of this study were to estimate the degree of thigh intramuscular fat in individuals without radiographic OA or frequent pain and assess the associations of age, sex, and BMI with the degree of intramuscular fat. Individuals without knee or hip radiographic OA, without total knee/hip arthroplasty, and without frequent knee/hip pain were selected from the OAI database (n = 710). Goutallier Grades (GGs) of the quadriceps and hamstring muscles were assessed based on 3 T MR images on a scale from 0 (normal muscle) to 4 (more fat than muscle). The associations between demographic variables and GG outcomes were evaluated using mixed effects models. The most prevalent GGs among the muscles were Grades 1 and 2; Grade 4 was infrequent (< 1%). Greater BMI (p < 0.001) and age (p < 0.001) were each associated with greater GG. Women had greater GG than men (greatest difference in the vastus medialis: coeff. = 0.214, p < 0.001). At lower BMI, women had greater intramuscular fat than men; at higher BMI, men had greater intramuscular fat than women (p = 0.029 for BMI-sex interaction). While individuals without radiographic OA or frequent pain generally had low thigh intramuscular fat, higher BMI and age were associated with greater intramuscular fat, and GGs were greater in women than men. The relationship between BMI and intramuscular fat was sex-dependent. Thus, demographic variables must be considered when evaluating intramuscular fat.
Structural basis of urea transport by Arabidopsis thaliana DUR3
Extreme heat affects blueberry pollen nutrition, bee health, and plant reproduction
Choosing fit-for-purpose biodiversity impact indicators for agriculture in the Brazilian Cerrado ecoregion
Abstract Understanding and acting on biodiversity loss requires robust tools linking biodiversity impacts to land use change, the biggest threat to terrestrial biodiversity. Here we estimate agriculture’s impact on the Brazilian Cerrado’s biodiversity using three approaches—countryside Species-Area Relationship, Species Threat Abatement and Restoration and Species Habitat Index. By using same input data, we show how indicator scope and design affects impact assessments and resulting decision-support. All indicators show agriculture expansion’s increasing pressure on biodiversity. Results suggest that metrics are complementary, providing distinctly different insight into biodiversity change drivers and impacts. Meaningful applications of biodiversity indicators therefore require compatibility between focal questions and indicator choice regarding temporal, spatial, and ecological perspectives on impact and drivers. Backward-looking analyses focused on historical land use change and accountability are best served by the countryside-Species Area Relationship and the Species Habitat Index. Forward-looking analyses of impact risk hotspots and global extinctions mitigation are best served by the Species Threat Abatement and Restoration.
Comparative analysis of managerial strategies for enhancing teacher motivation in Public and Private Schools
Bacteria invade the brain following intracortical microelectrode implantation, inducing gut-brain axis disruption and contributing to reduced microelectrode performance
Abstract Brain-machine interface performance can be affected by neuroinflammatory responses due to blood-brain barrier (BBB) damage following intracortical microelectrode implantation. Recent findings suggest that certain gut bacterial constituents might enter the brain through damaged BBB. Therefore, we hypothesized that damage to the BBB caused by microelectrode implantation could facilitate microbiome entry into the brain. In our study, we found bacterial sequences, including gut-related ones, in the brains of mice with implanted microelectrodes. These sequences changed over time. Mice treated with antibiotics showed a reduced presence of these bacteria and had a different inflammatory response, which temporarily improved microelectrode recording performance. However, long-term antibiotic use worsened performance and disrupted neurodegenerative pathways. Many bacterial sequences found were not present in the gut or in unimplanted brains. Together, the current study established a paradigm-shifting mechanism that may contribute to chronic intracortical microelectrode recording performance and affect overall brain health following intracortical microelectrode implantation.
Retarding human adipose-derived MSCs senescence and promoting tendon repair using cell sheet engineering with a histone methyltransferase inhibitor
AIF3 splicing variant elicits mitochondrial malfunction via the concurrent dysregulation of electron transport chain and glutathione-redox homeostasis
QbD-steered HPTLC approach for concurrent estimation of six co-administered COVID-19 and cardiovascular drugs in different matrices: greenness appraisal
Abstract Many COVID-19 sufferers have a history of cardiovascular illnesses, which makes them more likely to develop severe COVID-19. Such patients were advised by experts to prioritize drug therapies based on their doctor’s commendations to avoid exacerbating their basic illnesses. Therefore, developing an analytical methodology for the concurrent estimation of medications prescribed for co-treating cardiovascular and COVID-19 illnesses is becoming critical in both bioavailability hubs and QC units. Herein, an inventive, rapid, and affordable HPTLC approach was developed, and its conditions were optimized employing the full factorial design approach for the concurrent estimation of aspirin, atorvastatin, atenolol, losartan, remdesivir, and favipiravir as co-administered medications, either with salicylic acid or not. Using the desirability function, the experimental design approach could forecast the best eluent system for optimal resolution results. On HPTLC-silica plates, the above-mentioned medications were separated utilizing an eluent system of ethyl acetate, methylene chloride, methanol, and ammonia (6:4:4:1 by volume), and their spots were detected at 232 nm. The proposed methodology was evaluated following ICH prerequisites and applied successfully to the medications’ dosage forms, human plasma, and buffered dissolution media with superb recovery proportions and no intrusiveness from formulations’ additives or plasma matrices. Five metrics were employed to appraise the suggested technique’s greenness: AGREE, eco-scale, Raynie and Driver, GAPI, and NEMI. The sensitivity, large sample capacity, and short run duration (15 min) of the proposed methodology confirm its appositeness for regular estimation of the above-mentioned medications.