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The nature of renewable energy resources (RERs), such as wind energy, makes them highly unstable, unpredictable, and intermittent. As a result, they must be optimized to reduce costs and emissions, increase reliability, and also to find the optimal size and location for RERs and energy storage systems (ESSs). Microgrids (MG) can be modified using ESSs to gradually reduce traditional energy use. In order to integrate RERs in a financially viable scheme, ESSs should be sized and operated optimally. The paper presents an enhanced biogeography-driven optimization algorithm for optimizing the operations and sizes of battery ESSs (BESSs) taking into account MGs that experience wind energy penetration in a way that migration rates are adaptively adjusted based on habitat suitability indexes and differential perturbations added to migration operators. An optimization problem was applied to a BESS to determine its depth of discharge and lifespan. This paper considers three different scenarios in using simulations and compares them to existing optimization methods for the purpose of demonstrating the effectiveness of the offered scheme. Out of all the case studies examined, the optimized BESS-linked case study was the least expensive. We also show that a BESS must be of an optimum size to function both economically and healthily. For economic and efficient functioning of MGs, it has been shown that finding the optimum size of the ESS is important and potentially extends battery lifespan. The IBBOA obtained a more precise size for BESS's volume, and the final outcomes are compared in this paper with other methods. [ABSTRACT FROM AUTHOR]

Based on the successful application of generative adversarial network (GAN) models in the field of image generation, this article introduces GANs into the field of deep learning for communication systems and surveys its application in modulation classification. To solve the difficulties in feature extraction, to address the low recognition accuracy of existing radio signal modulation‐type recognition methods, and to adapt to complex electromagnetic environments with high noise interference intensity, this article presents a modulation recognition model for high‐order digital signals. This model uses the Morlet wavelet transform to analyse time‐frequency signals, uses the excellent image generation performance of a GAN model to extract and reconstruct the features of noise‐contaminated time‐frequency images, and designs an integrated classification network architecture to classify and predict reconstructed images. The experimental results show that the algorithm model proposed in this article can significantly improve the recognition accuracy of high‐order digital modulated signals under low signal‐to‐noise ratio conditions and can achieve 90% recognition accuracy at a signal‐to‐noise ratio of 1 dB. [ABSTRACT FROM AUTHOR]

In order to improve the fault diagnosis accuracy of the electric locomotive inverter, this article combines the adversarial neural network to construct the electric locomotive inverter diagnosis system. Moreover, at the data level, this article compares and analyzes three methods of data expansion based on single-sample processing, data expansion based on image front and background separation, and data expansion based on an adversarial neural network. In addition, this article adopts a new feature extractor and increases the penalty cost of small samples being misclassified. Finally, this article uses the LBP operator to extract the image texture features to distinguish and detect the different shapes of the rotor windings and build an intelligent system to verify the effect of the proposed system model. The experimental research shows that the inverter diagnosis system for electric locomotives based on the proposed adversarial neural network has a good practical effect. [ABSTRACT FROM AUTHOR]

Unimodular waveforms with good correlation properties are desired for multiple‐input‐multiple‐output radar to achieve an increased transmitting/receiving virtual aperture. In this study, a new optimisation framework is introduced to design waveforms with good correlations. It is shown that many existing waveform design problems can be incorporated into this framework. Since the considered problem is in general highly non‐linear and non‐convex, an iterative optimisation method based on majorisation‐minimisation is developed. By carefully devising the surrogate function, the only requirement is to deal with a series of simple problems, which have closed‐form solutions. The proposed algorithm can be implemented via fast Fourier transform and hence is computationally efficient. In addition, the proposed algorithm is extended to deal with spectral constraints. Numerical results demonstrate the efficiency of the proposed algorithm compared with the state‐of‐the‐art techniques. [ABSTRACT FROM AUTHOR]

The ubiquitin ligase ring finger protein 5 (RNF5) has previously been associated with the development of breast cancer. Patients with breast cancer and high RNF5 expression have been demonstrated to have a shorter survival time compared with patients with low RNF5 expression. However, the role of RNF5 in human glioma has not been determined. The present study analyzed the role of RNF5 in gliomas using bioinformatics analysis. The results revealed that RNF5 was differentially expressed in non-cancerous brain tissues and different grades of glioma. Furthermore, a high RNF5 expression in patients with glioma was associated with an improved prognosis compared with patients with low expression. Gene Set Enrichment Analysis revealed that RNF5 was particularly associated with 'Wnt signaling pathway', 'apoptosis', 'focal adhesion' and 'cytokine-cytokine receptor interaction' in patients with glioma. Additionally, 4 potential ubiquitination substrates for RNF5 were predicted, including sorting nexin 10, proprotein convertase subtilisin/kexin type 1, leucine rich glioma inactivated 1 and solute carrier family 39 member 12. These findings provided the basis for further investigation on the role of RNF5 in tumors. [ABSTRACT FROM AUTHOR]

The rapid development of metastatic lesions remains the leading cause of mortality for patients with osteosarcoma. CD155 serves a key role in cancer cell migration, invasion and metastasis. However, the function and mechanism of CD155 has not been explored in osteosarcoma metastasis. In the present study, we found that CD155 was significantly upregulated in lung metastatic tissue and the highly metastatic cell line K7M2-WT (K7M2) of osteosarcoma. Overexpression of CD155 in K7M2 cells enhanced lung metastasis, while inhibition of CD155 by an anti-CD155 monoclonal antibody reduced metastasis. Blocking of CD155 also decreased migration and invasion of K7M2 cells in vitro. A western blot analysis revealed that blocking of CD155 inhibits metastasis by downregulating focal adhesion kinase (FAK) and phosphorylated FAK (pFAK) in osteosarcoma. The results revealed that CD155 serves a crucial role in the metastasis of osteosarcoma by regulating FAK and may provide a novel molecular target for therapeutic intervention in metastatic osteosarcoma. [ABSTRACT FROM AUTHOR]

Action recognition is usually a central problem for many practical applications, such as video annotations, video surveillance and human computer interaction. Most action recognition approaches are based on localized spatio-temporal features that can vary significantly when the viewpoint changes. However, their performance rapidly drops when the viewpoints of the training and testing data are different. In this paper, we propose a transfer learning framework for view-invariant action recognition by the way of sharing image stitching feature among different views. Experimental results on multi-view action recognition IXMAS dataset demonstrate that our method produces remarkably good results and outperforms baseline methods. [ABSTRACT FROM AUTHOR]

As an emerging class of spatial trajectory data, mobile user trajectory data can be used to analyze individual or group behavioral characteristics, hobbies and interests. Besides, the information extracted from original trajectory data is widely used in smart cities, transportation planning, and anti-terrorism maintenance. In order to identify the important locations of the target user from his trajectory data, a novel division method for preprocessing trajectory data is proposed, the feature points of original trajectory are extracted according to the change of trajectory structural, and then important locations are extracted by clustering the feature points, using an improved density peak clustering algorithm. Finally, in order to predict next location of mobile users, a multi-order fusion Markov model based on the Adaboost algorithm is proposed, the model order k is adaptively determined, and the weight coefficients of the 1~k-order models are given by the Adaboost algorithm according to the importance of various order models, a multi-order fusion Markov model is generated to predict next important location of the user. The experimental results on the real user trajectory dataset Geo-life show that the prediction performance of Adaboost-Markov model is better than the multi-order fusion Markov model with equal coefficient, and the universality and prediction performance of Adaboost-Markov model is better than the first to third order Markov models. [ABSTRACT FROM AUTHOR]

Accumulating evidence has shown that PI3K/Akt pathway is frequently hyperactivated in osteosarcoma (OS) and contributes to tumor initiation and progression. Altered phenotype of glucose metabolism is a key hallmark of cancer cells including OS. However, the relationship between PI3K/Akt pathway and glucose metabolism in OS remains largely unexplored. In this study, we showed that elevated Hexokinase-2 (HK2) expression, which catalyzes the first essential step of glucose metabolism by conversion of glucose into glucose-6-phosphate, was induced by activated PI3K/Akt signaling. Immunohistochemical analysis showed that HK2 was overexpressed in 83.3% (25/30) specimens detected and was closely correlated with Ki67, a cell proliferation index. Silencing of endogenous HK2 resulted in decreased aerobic glycolysis as demonstrated by reduced glucose consumption and lactate production. Inhibition of PI3K/Akt signaling also suppressed aerobic glycolysis and this effect can be reversed by reintroduction of HK2. Furthermore, knockdown of HK2 led to increased cell apoptosis and reduced ability of colony formation; meanwhile, these effects were blocked by 2-Deoxy- d -glucose (2-DG), a glycolysis inhibitor through its actions on hexokinase, indicating that HK2 functions in cell apoptosis and growth were mediated by altered aerobic glycolysis. Taken together, our study reveals a novel relationship between PI3K/Akt signaling and aerobic glycolysis and indicates that PI3K/Akt/HK2 might be potential therapeutic approaches for OS. [ABSTRACT FROM AUTHOR]

Pediatric high-grade glioma (HGG), including diffuse intrinsic pontine glioma (DIPG) are highly aggressive tumors with no effective cures. Lack of understanding of the molecular biology of these tumors, in part due to lack of well-characterized pre-clinical models, is a great challenge in the development of novel therapies. Recent studies have shown that pediatric HGG short-term cell cultures retain many of the tumor characteristics in vivo and at present one of the best choices for in-vivo experimental studies. The present review article would put light on novel genetic and epigenetic changes in pediatric HGG that might, act as a gold standard potential biomarkers and/or therapeutic targets in near future.

Acute patellar dislocation affects approximately 1:1000 healthy children 9-15 years of age, and up to 50% are at risk for recurrent dislocations. In adults the condition is associated with long-term complications, such as osteoarthritis and impairment of knee function. However, literature describing the outcome in a pediatric population is sparse. The present review article evaluates the long-term effects on knee function and cartilage quality after traumatic patellar dislocation in childhood, and also to evaluate the reliability of two clinical tests of medio-lateral knee position, in healthy children.

The determination of plausibility of an injury arising from a fall leading to head trauma is a great challenge especially in young children. The present review is aimed to discuss important developments in the filed of head trauma cases especially in children. We explored various studies pertaining to head trauma injuries in children by exploring mainly PubMed, Google scholar and some library periodicals available in our library. Studies in the recent past explored the head injuries as a result of a low height fall. However, there are great amount of difficulties in assessment of height with certainty that caused head injuries like skull fracture or intracranial injury. Biomechanical thresholds have been estimated for young children for injuries such as skull fracture, but they have not been assessed against the injuries observed in a clinical setting. So, this review discusses current aspects of pediatric head injuries ranging from a minor head injury to a skull fracture. The present review concludes that recording full details of cause of head trauma such as fall height is essential for proper treatment planning and efficient management.

Approximately 25% of osteosarcoma patients present with clinically detectable metastatic disease at the time of initial diagnosis. High-dose chemotherapy and/or surgery for the treatment of primary metastatic osteosarcoma is ineffective, and <20% of patients will survive 5 years from diagnosis. Therefore, the treatment of metastases is critical for the improvement of the prognosis of primary metastatic osteosarcoma patients. We have previously observed that overexpression of interleukin-24 (IL-24) inhibits neuroblastoma cell proliferation, migration and invasion in vitro . The present study investigated whether IL-24 may be a novel agent for osteosarcoma metastasis-suppressive treatment. It was observed that IL-24 is able to inhibit migration and invasion in spontaneously metastasizing human 143B osteosarcoma cells via the c-Jun N-terminal kinase (JNK)/c-Jun signaling pathway. IL-24 was effective in inhibiting JNK and c-Jun phosphorylation to downregulate matrix metalloproteinase (MMP)-2 and MMP-9, which contributed to the suppression of cell migration and invasion. It was concluded that IL-24 may be a potent agent in the inhibition of highly metastatic 143B osteosarcoma cells, and IL-24 may have translational potential as an effective therapeutic agent for the treatment of metastatic osteosarcoma.

Neuroblastomas are common pediatric solid tumors with a variable clinical course; approximately 50% of patients present with metastatic disease at diagnosis. The development of metastatic lesions often causes a fatal outcome. Therefore, the prevention of metastases during the early stage of tumor development is critical for the improvement of the prognosis of neuroblastoma patients. We previously observed the suppression of neuroblastoma growth in response to overexpression of interleukin-24 (IL-24) in vitro and in vivo. IL-24 exerts its tumor-suppressive effects by multiple mechanisms, including the balance of Bcl-2 family proteins toward the pro-apoptotic pathway and the activation of the caspase cascade. In this study, we used adenovirus-mediated IL-24 (Ad-IL24) to examine the effect of the ectopic production of IL-24 on cell migration and invasion in human neuroblastoma cells. We found that IL-24 effectively inhibits SH-SY5Y neuroblastoma cell migration and invasion by changing subcellular localization and cellular levels of β-catenin and regulating the levels of proteins associated with cell migration and invasion. Thus, IL-24 should be considered a therapeutic agent that can inhibit primary neuroblastoma growth and that may prevent metastasis.