Landfills are designed to dispose high quantities of waste at economical costs with potentially less environmental effects; however, improper landfill management may pose serious environmental ...threats through discharge of high strength polluted wastewater also known as leachate. This paper focused on achievements on landfill leachate treatment by different technology, which contains biological treatment and membrane technology. Finally, development and prospect of landfill leachate treatment were predicted.
As is known, early prediction of thermal load in buildings can give valuable insight to engineers and energy experts in order to optimize the building design. Although different machine learning ...models have been promisingly employed for this problem, newer sophisticated techniques still require proper attention. This study aims at introducing novel hybrid algorithms for estimating building thermal load. The predictive models are artificial neural networks exposed to five optimizer algorithms, namely Archimedes optimization algorithm (AOA), Beluga whale optimization (BWO), forensic-based investigation (FBI), snake optimizer (SO), and transient search algorithm (TSO), for attaining optimal trainings. These five integrations aim at predicting the annual thermal energy demand. The accuracy of the models is broadly assessed using mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R.sup.2) indicators and a ranking system is accordingly developed. As the MAPE and R.sup.2 reported, all obtained relative errors were below 5% and correlations were above 92% which confirm the general acceptability of the results and all used models. While the models exhibited different performances in training and testing stages, referring to the overall results, the BWO emerged as the most accurate algorithm, followed by the AOA and SO simultaneously in the second position, the FBI as the third, and TSO as the fourth accurate model. Mean absolute error (MAPE) and Considering the wide variety of artificial intelligence techniques that are used nowadays, the findings of this research may shed light on the selection of proper techniques for reliable energy performance analysis in complex buildings.
Advanced glycation end products (AGEs) are formed in non-enzymatic reaction, oxidation, rearrangement and cross-linking between the active carbonyl groups of reducing sugars and the free amines of ...amino acids. The Maillard reaction is related to sensory characteristics in thermal processed food, while AGEs are formed in food matrix in this process. AGEs are a key link between carbonyl stress and neurodegenerative disease. AGEs can interact with receptors for AGEs (RAGE), causing oxidative stress, inflammation response and signal pathways activation related to neurodegenerative diseases. Neurodegenerative diseases are closely related to gut microbiota imbalance and intestinal inflammation. Polyphenols with multiple hydroxyl groups showed a powerful ability to scavenge ROS and capture α-dicarbonyl species, which led to the formation of mono- and di- adducts, thereby inhibiting AGEs formation. Neurodegenerative diseases can be effectively prevented by inhibiting AGEs production, and interaction with RAGEs, or regulating the microbiota-gut-brain axis. These strategies include polyphenols multifunctional effects on AGEs inhibition, RAGE-ligand interactions blocking, and regulating the abundance and diversity of gut microbiota, and intestinal inflammation alleviation to delay or prevent neurodegenerative diseases progress. It is a wise and promising strategy to supplement dietary polyphenols for preventing neurodegenerative diseases via AGEs-RAGE axis and microbiota-gut-brain axis regulation.
This paper focuses on the theme of the application of deep learning in the field of basketball sports, using research methods such as literature research, video analysis, comparative research, and ...mathematical statistics to explore deep learning in real-time analysis of basketball sports data. The basketball posture action recognition and analysis system proposed for basketball movement is composed of two parts serially. The first part is based on the bottom-up posture estimation method to locate the joint points and is used to extract the posture sequence of the target in the video. The second part is the analysis and research of the action recognition algorithm based on the convolution of the space-time graph. According to the extracted posture sequence, the basketball action of the set classification is recognized. In order to obtain more accurate and three-dimensional information, a multitraining target method can be used in training; that is, multiple indicators can be detected and feedback is provided at the same time to correct player errors in time; the other is an auxiliary method, which is compared with ordinary training. The method can actively correct technical movements, train players to form muscle memory, and improve their abilities. Through the research of this article, it provides a theoretical basis for promoting the application of deep learning in the field of basketball and also provides a theoretical reference for the wider application of deep learning in the field of sports. At the same time, the designed real-time analysis system of basketball data also provides more actual reference values for coaches and athletes.
Memristor-enabled neuromorphic computing systems provide a fast and energy-efficient approach to training neural networks
. However, convolutional neural networks (CNNs)-one of the most important ...models for image recognition
-have not yet been fully hardware-implemented using memristor crossbars, which are cross-point arrays with a memristor device at each intersection. Moreover, achieving software-comparable results is highly challenging owing to the poor yield, large variation and other non-ideal characteristics of devices
. Here we report the fabrication of high-yield, high-performance and uniform memristor crossbar arrays for the implementation of CNNs, which integrate eight 2,048-cell memristor arrays to improve parallel-computing efficiency. In addition, we propose an effective hybrid-training method to adapt to device imperfections and improve the overall system performance. We built a five-layer memristor-based CNN to perform MNIST
image recognition, and achieved a high accuracy of more than 96 per cent. In addition to parallel convolutions using different kernels with shared inputs, replication of multiple identical kernels in memristor arrays was demonstrated for processing different inputs in parallel. The memristor-based CNN neuromorphic system has an energy efficiency more than two orders of magnitude greater than that of state-of-the-art graphics-processing units, and is shown to be scalable to larger networks, such as residual neural networks. Our results are expected to enable a viable memristor-based non-von Neumann hardware solution for deep neural networks and edge computing.
China’s stem cell research policies are based on a cultural understanding that grants special protection to human embryos but does not assign them equivalent moral or legal status as fully developed ...humans. We discuss ethical considerations for embryo research and policy changes as China moves toward adopting internationally recognized rules.
China’s stem cell research policies are based on a cultural understanding that grants special protection to human embryos but does not assign them equivalent moral or legal status as fully developed humans. We discuss ethical considerations for embryo research and policy changes as China moves toward adopting internationally recognized rules.
We perform a detailed analysis of the solvability of linear strain equations on several non-characteristic regions to obtain L2 regularity solutions. As an application, the rigidity results on the ...strain tensor of the middle surface are implied by the L2 regularity for non-characteristic regions. Finally, we obtain the optimal constant in the first Korn inequality scales like h4/3 for hyperbolic shells, generalizing the assumption that the middle surface of the shell is given by a single principal system in the literature.
Despite recent advances in diagnosis and treatment, osteosarcoma remains as the most common bone cancer in children and is associated with poor prognosis. Growing evidence has supported dysregulation ...of threonine and tyrosine protein kinase (TTK) expression as a hallmark of multiple cancers, however, its function in osteosarcoma remains to be elucidated. In the present study, we found that TTK was frequently overexpressed in osteosarcoma and associated with increased tumour growth and progression. Moreover, using both in vitro and in vivo assays, we provided evidence that TTK level was regulated by a molecular chaperone, heat shock protein 90 (Hsp90). Hsp90 directly interacted with TTK and prevents proteasome‐dependent TTK degradation, leading to the accumulation of TTK in osteosarcoma cells. Elevated TTK promoted cancer cell proliferation and survival by activating cell‐cycle progression and inhibiting apoptosis. Consistently, depletion of TTK by Hsp90 inhibition induced cell‐cycle arrest, generated aneuploidy and eventually resulted in apoptotic cancer cell death. Together, our study revealed an important Hsp90‐TTK regulatory axis in osteosarcoma cells to promote cancer cell growth and survival. These findings expand our knowledge on osteosarcoma pathogenesis and offer novel therapeutic options for clinical practice.
Conventional hardware platforms consume huge amount of energy for cognitive learning due to the data movement between the processor and the off-chip memory. Brain-inspired device technologies using ...analogue weight storage allow to complete cognitive tasks more efficiently. Here we present an analogue non-volatile resistive memory (an electronic synapse) with foundry friendly materials. The device shows bidirectional continuous weight modulation behaviour. Grey-scale face classification is experimentally demonstrated using an integrated 1024-cell array with parallel online training. The energy consumption within the analogue synapses for each iteration is 1,000 × (20 ×) lower compared to an implementation using Intel Xeon Phi processor with off-chip memory (with hypothetical on-chip digital resistive random access memory). The accuracy on test sets is close to the result using a central processing unit. These experimental results consolidate the feasibility of analogue synaptic array and pave the way toward building an energy efficient and large-scale neuromorphic system.
Herein, a label‐free and immobilization‐free electrochemiluminescence resonance energy transfer (ECL‐RET) system based on graphitic carbon nitride nanosheets (GCNNs)/Ru(phen)32+ donor/acceptor pair ...is developed, in which the ECL‐RET is regulated by regulating the diffusivity of Ru(phen)32+ molecules toward the negatively charged GCNNs through logically programmed DNA hybridization reactions. The two optical signals of GCNNs (445 nm) and Ru(phen)32+ (593 nm) show completely opposite changes through the same one‐time DNA hybridization reaction. Based on this ECL‐RET system, a contrary logic pair (CLP) library, a parity generator/checker system for differentiating the erroneous bits during data transmission, the parity checker to identify the even/odd natural numbers from 0 to 9, and a series of concatenated logic circuits including a six‐input logic gate capable of implementing of 64 input combinations for meeting the needs of computational complexity are developed. The ECL‐RET‐based molecular logic system avoids the time‐consuming, costly and inefficient labeling procedures and the laborious processes of immobilization, presenting great potential for building more complicated and advanced logic gates, and providing a refreshing inspiration for the construction of combinatorial logic circuits based on ECL method.
An electrochemiluminescence resonance energy transfer (ECL‐RET) system is proposed for constructing contrary logic pair (CLP) library, even^odd parity generator/checker (Pg/Pc) system and various concatenated logic circuits based on the intercalation of Ru(phen)32+ into double‐stranded DNA and its diffusion toward the negatively charged graphitic carbon nitride nanosheets (GCNNs).
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