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Global warming and extreme weather have increased most people's awareness of the problem of environmental destruction. In the domain of sustainable development, environmental governance has received considerable scholarly attention. However, protecting and improving the environment requires not only substantial capital investment but also cooperation among stakeholders. Therefore, based on the network structure of stakeholders, the best-worst method (BWM) and modified Vlsekriterijumska Optimizacija I Kompromisno Resenje method were combined to form an environmental co-governance assessment framework that can be used to evaluate the effects of various policies and identify strategies for further improvement through data analysis (henceforth the BWM-mV model). This mechanism is not only useful for evaluating the effectiveness of environmental governance policies but also for generating suggestions to enhance these policies. Hence, the BWM-mV model is particularly suitable for local governments with limited resources in time, money, or labor. Pingxiang City Government is currently subject to such limitations and was therefore selected as the subject of an empirical case study. The results of this study revealed that the aspects (i.e., criteria) the Pingxiang City Government should urgently improve on pertain to a high-quality information communication platform (C13) and smooth joint decision-making by stakeholders (C24).The widespread use of glyphosate as a herbicide in agriculture can lead to the presence of its residues and metabolites in food for human consumption and thus pose a threat to human health. It has been found that glyphosate reduces energy metabolism in the brain, its amount increases in white muscle fibers. At the same time, the effect of chronic use of glyphosate on the dynamic properties of skeletal muscles remains practically unexplored. The selected biomechanical parameters (the integrated power of muscle contraction, the time of reaching the muscle contraction force its maximum value and the reduction of the force response by 50% and 25% of the initial values during stimulation) of muscle soleus contraction in rats, as well as blood biochemical parameters (the levels of creatinine, creatine phosphokinase, lactate, lactate dehydrogenase, thiobarbituric acid reactive substances, hydrogen peroxide, reduced glutathione and catalase) were analyzed after chronic glyphosate intoxication (oral administration at a dose of 10 μg/kg of animal weight) for 30 days. Water-soluble C60 fullerene, as a poweful antioxidant, was used as a therapeutic nanoagent throughout the entire period of intoxication with the above herbicide (oral administration at doses of 0.5 or 1 mg/kg). The data obtained show that the introduction of C60 fullerene at a dose of 0.5 mg/kg reduces the degree of pathological changes by 40-45%. Increasing the dose of C60 fullerene to 1 mg/kg increases the therapeutic effect by 55-65%, normalizing the studied biomechanical and biochemical parameters. Thus, C60 fullerenes can be effective nanotherapeutics in the treatment of glyphosate-based herbicide poisoning.The genus Vibrio comprises pathogens ubiquitous to marine environments. This study evaluated the cultivable Vibrio community in the Guanabara Bay (GB), a recreational, yet heavily polluted estuary in Rio de Janeiro, Brazil. Over one year, 66 water samples from three locations along a pollution gradient were investigated. Isolates were identified by MALDI-TOF mass spectrometry, revealing 20 Vibrio species, including several potential pathogens. Antimicrobial susceptibility testing confirmed resistance to aminoglycosides, beta-lactams (including carbapenems and third-generation cephalosporins), fluoroquinolones, sulfonamides, and tetracyclines. Four strains were producers of extended-spectrum beta-lactamases (ESBL), all of which carried beta-lactam and heavy metal resistance genes. The toxR gene was detected in all V. parahaemolyticus strains, although none carried the tdh or trh genes. Higher bacterial isolation rates occurred in months marked by higher water temperatures, lower salinities, and lower phosphorus and nitrogen concentrations. The presence of non-susceptible Vibrio spp. was related to indicators of eutrophication and sewage inflow. DNA fingerprinting analyses revealed that V. harveyi and V. https://www.selleckchem.com/products/etomoxir-na-salt.html parahaemolyticus strains non-susceptible to antimicrobials might persist in these waters throughout the year. Our findings indicate the presence of antimicrobial-resistant and potentially pathogenic Vibrio spp. in a recreational environment, raising concerns about the possible risks of human exposure to these waters.In recent times, the usage of modern neuromorphic hardware for brain-inspired SNNs has grown exponentially. In the context of sparse input data, they are undertaking low power consumption for event-based neuromorphic hardware, specifically in the deeper layers. However, using deep ANNs for training spiking models is still considered as a tedious task. Until recently, various ANN to SNN conversion methods in the literature have been proposed to train deep SNN models. Nevertheless, these methods require hundreds to thousands of time-steps for training and still cannot attain good SNN performance. This work proposes a customized model (VGG, ResNet) architecture to train deep convolutional spiking neural networks. In this current study, the training is carried out using deep convolutional spiking neural networks with surrogate gradient descent backpropagation in a customized layer architecture similar to deep artificial neural networks. Moreover, this work also proposes fewer time-steps for training SNNs with surrogate gradient descent. During the training with surrogate gradient descent backpropagation, overfitting problems have been encountered. To overcome these problems, this work refines the SNN based dropout technique with surrogate gradient descent. The proposed customized SNN models achieve good classification results on both private and public datasets. In this work, several experiments have been carried out on an embedded platform (NVIDIA JETSON TX2 board), where the deployment of customized SNN models has been extensively conducted. Performance validations have been carried out in terms of processing time and inference accuracy between PC and embedded platforms, showing that the proposed customized models and training techniques are feasible for achieving a better performance on various datasets such as CIFAR-10, MNIST, SVHN, and private KITTI and Korean License plate dataset.
