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As semi-crystalline polyester (lactic acid) (PLA) is combined with other reinforcing materials, challenges such as phase separation, environmental pollution, and manufacturing difficulties could hinder the benefits of PLA, including complete biodegradability and strong mechanical properties. In the present investigation, melt blending is utilized to establish a mixture of low- and high-molecular-weight polylactic acids (LPLA and HPLA). The crystallinity, rheology, and mechanical properties of the combination were analyzed using rotational rheometry, differential scanning calorimetry, X-ray diffraction, polarized optical microscopy, scanning electron microscopy, and universal testing equipment. The results demonstrate compatibility between LPLA and HPLA. Moreover, an increase in LPLA concentration leads to a decrease in the crystallization rate, spherulite size, fractional crystallinity, and XRD peak intensity during isothermal crystallization. LPLA acts as a diluent during isothermal crystallization, whereas HPLA functions as a nucleating agent in the non-isothermal crystallization process, promoting the growth of LPLA crystals and leading to co-crystallization. The blended system with a 5% LPLA mass fraction exhibits the highest tensile strength and enhances rheological characteristics. By effectively leveraging the relationship between various molecular weights of PLA’s mechanical, rheological, and crystallization behavior, this scrutiny improves the physical and mechanical characteristics of the material, opening up new opportunities.

Recently, with the growth of electronic cars and the advancement of modern vehicles using portable equipment and embedded systems, several in-vehicle networks like the CAN (Controller Area Network) encountered novel risks of security. Because the portal of CAN does not have systems of security, like encryption and authentication in order to contend with cyber-attacks, the necessity for a system of intrusion detection for identifying attacks on the portal of CAN is really essential. In this study, Triple-attention Mechanism (TAN) has been used to recognize different kinds of security intrusions in portals of CAN. The purpose of TAN here is to identify intrusion within 3 steps. Within the initial phase, the major features have been extracted, and TAN functions as a descriptor of feature. Then, the discriminating categorizer classifies the current features. Eventually, with the help of adversarial learning, intrusion has been recognized. The current work utilizes a novel Greylag Goose Optimization algorithm for optimal selection of the network hyperparameters. For checking the effectiveness of the suggested method, an open-source dataset was applied, which recorded the traffic of CAN using a real vehicle throughout injection attacks of message. The results show that this method outperforms certain machine learning algorithms in error rate and false negative for DoS and drive gear and RPM spoofing attack with accuracy of 96.3%, recall of 96.1%, F1-Score of 96.2%, specificity of 97.2%, accuracy of 96.3%, AUC-ROC of 0.97, and MCC of 0.92 for DoS attacks. Therefore, the phase attack is minimized.

The study integrated the Theory of Planned Behavior, the Technology Acceptance Model, and the Uses and Gratification Theory to predict young American consumers' mobile apps attitudes, intent and use. The model was tested by a web survey of 555 American college students in winter, 2011. SEM results show that young American consumers' attitudes and intent predict their use of mobile applications. Perceived enjoyment, usefulness, ease of use and subjective norm emerge as significant predictors of their mobile apps attitudes. Perceived behavioral control, usefulness, and mobile Internet use predict their intent to use mobile applications. Their use of mobile applications is determined by perceived usefulness, intent to use, mobile Internet use, income and gender. Implications for academia and industry are discussed.

Cyclooxygenase-2 (COX-2), a rate-limiting enzyme converting arachidonic acid to prostaglandins and a key player in neuroinflammation, has been implicated in the pathogenesis of neurodegenerative diseases such as multiple sclerosis, Parkinsons and Alzheimers diseases, and in traumatic brain injury- and ischemia-induced neuronal damage, and epileptogenesis. Accumulated information suggests that the contribution of COX-2 to neuropathology is associated with its involvement in synaptic modification. Inhibition or elevation of COX-2 has been shown to suppress or enhance excitatory glutamatergic neurotransmission and long-term potentiation (LTP). These events are mainly mediated via PGE2, the predominant reaction product of COX-2, and the PGE2 subtype 2 receptor (EP2)-protein kinase A pathway. Recent evidence shows that endogenous cannabinoids are substrates for COX-2 and can be oxygenated by COX-2 to form new classes of prostaglandins (prostaglandin glycerol esters and prostaglandin ethanolamides). These COX-2 oxidative metabolites of endocannabinoids, as novel signaling mediators, modulate synaptic transmission and plasticity and cause neurodegeneration. The actions of these COX-2 metabolites are likely mediated by mitogen-activated protein kinase (MAPK) and inositol 1,4,5-trisphosphate (IP3) signal transduction pathways. These discoveries suggest that the contributions of COX-2 to neurotransmission and brain malfunction result not only from its conversion of arachidonic acid to classic prostaglandins but also from its oxidative metabolism of endocannabinoids to novel prostaglandins. Thus, elucidation of COX-2 in synaptic signaling may provide a mechanistic basis for designing new drugs aimed at preventing, treating or alleviating neuroinflammation-associated neurological disorders.

A total of 4,556 US college students were surveyed immediately after Election 2012 to investigate what social media–related psychological and behavioral factors predicted their online political participation. Structural equation modeling and hierarchical multiple regression results showed that online social capital, political self-efficacy, and Facebook group participation were positive predictors of online political participation, while social trust did not directly influence online political participation. General political use of Facebook and Twitter was a positive predictor of online political participation; however, extensive Facebook and Twitter use was a negative predictor. Implications for research and political practice are discussed.

Carlavirus sigmasolani (Potato virus S, PVS) is a globally distributed plant virus infecting cultivated potato ( Solanum tuberosum ), causing yield losses and reduced tuber quality in the host crop, yet its evolutionary history, global dissemination and population genetic structure remain incompletely understood. In this study, we conducted comprehensive phylogenetic and Bayesian phylogeographic analyses of PVS using all available complete genome and coat protein (CP) gene sequences from 35 countries. Genome-based phylogenetic reconstruction identified four major phylogroups (I–IV), with Phylogroup I comprising only Colombian isolates and Phylogroup IV showing the broadest geographic distribution. In contrast, CP gene-based analyses revealed seven phylogroups (I–VII), including regionally restricted Phylogroups V (Colombia) and VI (Ecuador), and the globally dominant Phylogroup VII. A time-scaled Bayesian phylogenetic framework estimated a mean substitution rate of 3.11 × 10 -4 substitutions/site/year (95% HPD: 2.19 × 10 -4 –4.07 × 10 -4 ), and dated the most recent common ancestor (tMRCA) of PVS to approximately 1296 (95% HPD: 964–1578). Phylogeographic analysis based on CP gene sequences suggests that Ecuador is a likely center of origin for PVS, with intercontinental dissemination beginning in the 16th century and markedly accelerating during the 19th and 20th centuries. Iran and China were identified as major secondary hubs during this period, while Europe and the United States also contributed to global dissemination as important intercontinental transmission centers during the 20th and 21st centuries. Population genetic analyses indicated that South America retains the highest diversity, reinforcing its status as the center of origin, while the markedly lower diversity in Africa and Oceania suggests more recent introductions coupled with restricted gene flow. These data improve our understanding of PVS evolution, spread and population structure, supporting the development of effective monitoring and control strategies.

Scale dependence of surface roughness of natural rock joints has long been an outstanding issue in rock mechanics. Controversial results were reported by various studies; however, the nature of scale dependency and the underlying mechanism for the conflicting observations remain unclear. Rock joints at different scales characterise two-order asperities, namely, waviness and unevenness; thus understanding how the individual roughness of waviness and unevenness vary as the joint size increases from the laboratory-scale to the large-scale is crucial for revealing the scale effect mystery. Here we digitise three natural granite joint surfaces with the same dimension of 1000 mm × 1000 mm through a high-resolution, three-dimensional scanner. Waviness and unevenness of each rock joint surface are quantitatively separated by selecting an appropriate sampling interval. The respective fractal dimensions of waviness and unevenness of joint surfaces sized from 100 mm × 100 mm to 1000 mm × 1000 mm are estimated through an improved roughness-length method. We find that the fractal dimensions of two-order roughness are scale-dependent but without generalised trends. The stationarity threshold beyond which the scale-dependency of roughness vanishes is absent for all the three joint samples, suggesting that the roughness of natural rock joints be assessed at the specific scale of the rock mass in-situ. We reveal that previous controversial results regarding scale effect are likely due to the composition of the roughness scaling of waviness and unevenness. Thus, accurate stability analysis of rock-engineering projects should consider separate contributions of multi-order asperities across scales to the strength and deformation of jointed rock masses.

Flying safely in complex urban environments is a challenge for unmanned aerial vehicles because path planning in urban environments with many narrow passages and few dynamic flight obstacles is difficult. The path planning problem is decomposed into global path planning and local path adjustment in this paper. First, a branch-selected rapidly-exploring random tree (BS-RRT) algorithm is proposed to solve the global path planning problem in environments with narrow passages. A cyclic pruning algorithm is proposed to shorten the length of the planned path. Second, the GM(1,1) model is improved with optimized background value named RMGM(1,1) to predict the flight path of dynamic obstacles. Herein, the local path adjustment is made by analyzing the prediction results. BS-RRT demonstrated a faster convergence speed and higher stability in narrow passage environments when compared with RRT, RRT-Connect, P-RRT, 1-0 Bg-RRT, and RRT*. In addition, the path planned by BS-RRT through the use of the cyclic pruning algorithm was the shortest. The prediction error of RMGM(1,1) was compared with those of ECGM(1,1), PCGM(1,1), GM(1,1), MGM(1,1), and GDF. The trajectory predicted by RMGM(1,1) was closer to the actual trajectory. Finally, we use the two methods to realize path planning in urban environments.

In order to address the problem of data heterogeneity, in recent years, personalized federated learning has tailored models to individual user data to enhance model performance on clients with diverse data distributions. However, the existing personalized federated learning methods do not adequately address the problem of data heterogeneity, and lack the processing of system heterogeneity. Consequently, these issues lead to diminished training efficiency and suboptimal model performance of personalized federated learning in heterogeneous environments. In response to these challenges, we propose FedPRL, a novel approach to personalized federated learning designed specifically for heterogeneous environments. Our method tackles data heterogeneity by implementing a personalized strategy centered on local data storage, enabling the accurate extraction of features tailored to the data distribution of individual clients. This personalized approach enhances the performance of federated learning models when dealing with non-IID data. To overcome system heterogeneity, we design a client selection mechanism grounded in reinforcement learning and user quality evaluation. This mechanism optimizes the selection of clients based on data quality and training time, thereby boosting the efficiency of the training process and elevating the overall performance of personalized models. Moreover, we devise a local training method that utilizes global knowledge distillation of non-target classes, which combined with traditional federated learning can effectively address the issue of catastrophic forgetting during global model updates. This approach enhances the generalization capability of the global model and further improves the performance of personalized models. Extensive experiments on both standard and real-world datasets demonstrate that FedPRL effectively resolves the challenges of data and system heterogeneity, enhancing the efficiency and model performance of personalized federated learning methods in heterogeneous environments, and outperforming state-of-the-art methods in terms of model accuracy and training efficiency.

Acupuncture is one of the most promising modalities in complimentary medicine. However, the underlying mechanisms are not well understood yet. We found that in TRPV2 knockout male mice, acupuncture-induced analgesia was suppressed with a decreased activation of mast cells in the acupoints stimulated. The mast cell stabilizer sodium cromolyn could suppress the release of adenosine in the acupoints on male rats. A direct injection of adenosine A1 receptor agonist or histamine H1 receptor agonist increased β-endorphin in the cerebral-spinal fluid in the acute adjuvant arthritis male rats and thus replicated the analgesic effect of acupuncture. These observations suggest that the mast cell is the central structure of acupoints and is activated by acupuncture through TRPV2 channels. The mast cell transduces the mechanical stimuli to acupuncture signal by activating either H1 or A1 receptors, therefore triggering the acupuncture effect in the subject. These findings might open new frontiers for acupuncture research.