Image Super-Resolution Based on Sparse Coding with Multi-Class Dictionaries

Sparse coding-based single image super-resolution has attracted much interest. In this paper, a super-resolution reconstruction algorithm based on sparse coding with multi-class dictionaries is put forward. We propose a novel method for image patch classification, using the phase congruency information. A sub-dictionary is learned from patches in each category. For a given image patch, the sub-dictionary that belongs to the same category is selected adaptively. Since the given patch has similar pattern with the selected sub-dictionary, it can be better represented. Finally, iterative back-projection is used to enforce global reconstruction constraint. Experiments demonstrate that our approach can produce comparable or even better super-resolution reconstruction results with some existing algorithms, in both subjective visual quality and numerical measures

Effects of Simvastatin on Glucose Metabolism in Mouse MIN6 Cells

The aim of this study was to investigate the effects of simvastatin on insulin secretion in mouse MIN6 cells and the possible mechanism. MIN6 cells were, respectively, treated with 0 μM, 2 μM, 5 μM, and 10 μM simvastatin for 48 h. Radio immunoassay was performed to measure the effect of simvastatin on insulin secretion in MIN6 cells. Luciferase method was used to examine the content of ATP in MIN6 cells. Real-time PCR and western blotting were performed to measure the mRNA and protein levels of inward rectifier potassium channel 6.2 (Kir6.2), voltage-dependent calcium channel 1.2 (Cav1.2), and glucose transporter-2 (GLUT2), respectively. ATP-sensitive potassium current and L-type calcium current were recorded by whole-cell patch-clamp technique. The results showed that high concentrations of simvastatin (5 μM and 10 μM) significantly reduced the synthesis and secretion of insulin compared to control groups in MIN6 cells (P<0.05). ATP content in simvastatin-treated cells was lower than in control cells (P<0.05). Compared with control group, the mRNA and protein expression of Kir6.2 increased with treatment of simvastatin (P<0.05), and mRNA and protein expression of Cav1.2 and GLUT2 decreased in response to simvastatin (P<0.05). Moreover, simvastatin increased the ATP-sensitive potassium current and reduced the L-type calcium current. These results suggest that simvastatin inhibits the synthesis and secretion of insulin through a reduction in saccharometabolism in MIN6 cells

Evaluatology: The Science and Engineering of Evaluation

Evaluation is a crucial aspect of human existence and plays a vital role in various fields. However, it is often approached in an empirical and ad-hoc manner, lacking consensus on universal concepts, terminologies, theories, and methodologies. This lack of agreement has significant repercussions. This article aims to formally introduce the discipline of evaluatology, which encompasses the science and engineering of evaluation. We propose a universal framework for evaluation, encompassing concepts, terminologies, theories, and methodologies that can be applied across various disciplines. Our research reveals that the essence of evaluation lies in conducting experiments that intentionally apply a well-defined evaluation condition to diverse subjects and infer the impact of different subjects by measuring and/or testing. Derived from the essence of evaluation, we propose five axioms focusing on key aspects of evaluation outcomes as the foundational evaluation theory. These axioms serve as the bedrock upon which we build universal evaluation theories and methodologies. When evaluating a single subject, it is crucial to create evaluation conditions with different levels of equivalency. By applying these conditions to diverse subjects, we can establish reference evaluation models. These models allow us to alter a single independent variable at a time while keeping all other variables as controls. When evaluating complex scenarios, the key lies in establishing a series of evaluation models that maintain transitivity. Building upon the science of evaluation, we propose a formal definition of a benchmark as a simplified and sampled evaluation condition that guarantees different levels of equivalency. This concept serves as the cornerstone for a universal benchmark-based engineering approach to evaluation across various disciplines, which we refer to as benchmarkology.Comment: 29 pages, 16 figures, and 2 table

A Spatio-Temporal Deep Learning Network for the Short-Term Energy Consumption Prediction of Multiple Nodes in Manufacturing Systems

Short-term energy prediction plays an important role in green manufacturing in the industrial internet environment and has become the basis of energy wastage identification, energy-saving plans and energy-saving control. However, the short-term energy prediction of multiple nodes in manufacturing systems is still a challenging issue owing to the fuzzy material flow (spatial relationship) and the dynamic production rhythm (temporal relationship). To obtain the complex spatial and temporal relationships, a spatio-temporal deep learning network (STDLN) method is presented for the short-term energy consumption prediction of multiple nodes in manufacturing systems. The method combines a graph convolutional network (GCN) and a gated recurrent unit (GRU) and predicts the future energy consumption of multiple nodes based on prior knowledge of material flow and the historical energy consumption time series. The GCN is aimed at capturing spatial relationships, with the material flow represented by a topology model, and the GRU is aimed at capturing dynamic rhythm from the energy consumption time series. To evaluate the method presented, several experiments were performed on the power consumption dataset of an aluminum profile plant. The results show that the method presented can predict the energy consumption of multiple nodes simultaneously and achieve a higher performance than methods based on the GRU, GCN, support vector regression (SVR), etc.</jats:p

A Spatio-Temporal Deep Learning Network for the Short-Term Energy Consumption Prediction of Multiple Nodes in Manufacturing Systems

Short-term energy prediction plays an important role in green manufacturing in the industrial internet environment and has become the basis of energy wastage identification, energy-saving plans and energy-saving control. However, the short-term energy prediction of multiple nodes in manufacturing systems is still a challenging issue owing to the fuzzy material flow (spatial relationship) and the dynamic production rhythm (temporal relationship). To obtain the complex spatial and temporal relationships, a spatio-temporal deep learning network (STDLN) method is presented for the short-term energy consumption prediction of multiple nodes in manufacturing systems. The method combines a graph convolutional network (GCN) and a gated recurrent unit (GRU) and predicts the future energy consumption of multiple nodes based on prior knowledge of material flow and the historical energy consumption time series. The GCN is aimed at capturing spatial relationships, with the material flow represented by a topology model, and the GRU is aimed at capturing dynamic rhythm from the energy consumption time series. To evaluate the method presented, several experiments were performed on the power consumption dataset of an aluminum profile plant. The results show that the method presented can predict the energy consumption of multiple nodes simultaneously and achieve a higher performance than methods based on the GRU, GCN, support vector regression (SVR), etc

Comparative Study of Water Flow in Nanopores with Different Quartz (101&macr;0) Surfaces via Molecular Dynamics Simulations

Dewatering and gas production are applied on a large scale in shale gas development. The fundamental mechanisms of water flow in shale nanoporous media are essential for the development of shale oil and gas resources. In this work, we use molecular dynamic simulations to investigate water flow in two different quartz surface ((101&macr;0)-&alpha; and (101&macr;0)-&beta;) nanopores. Results show that the (101&macr;0)-&beta; surface exhibits stronger water molecule structuring with a structure arranged in two layers and higher first-layer adsorption density (2.44 g/cm3) compared to the ((101&macr;0)-&alpha; surface (1.68 g/cm&sup3;). The flow flux under the (101&macr;0)-&alpha; surface is approximately 1.2 times higher than that under the (101&macr;0)-&beta; surface across various pressure gradients. We developed a theoretical model dividing the pore space into non-flowing, adsorbed, and bulk water regions, with critical thicknesses of 0.14 nm and 0.27 nm for the non-flowing region, and 0.15 nm for the adsorbed region in both surfaces. This model effectively predicts velocity distributions and volumetric flow rates with errors generally below 5%. Our findings provide insights into water transport mechanisms in shale inorganic nanopores and offer practical guidance for numerical simulation of shale gas production through dewatering operations

Could bibliometrics reveal top science and technology achievements and researchers? The case for evaluatology-based science and technology evaluation

By utilizing statistical methods to analyze bibliographic data, bibliometrics faces inherent limitations in identifying the most significant science and technology achievements and researchers. To overcome this challenge, we present an evaluatology-based science and technology evaluation methodology. At the heart of this approach lies the concept of an extended evaluation condition (EC), encompassing nine crucial components derived from a field. We define four relationships that illustrate the connections among various achievements based on their mapped extended EC components, as well as their temporal and citation links. Within a relationship under an extended EC, evaluators can effectively compare these achievements by carefully addressing the influence of confounding variables. We establish a real-world evaluation system encompassing an entire collection of achievements, each of which is mapped to several components of an extended EC. Within a specific field like chip technology or open source, we construct a perfect evaluation model that can accurately trace the evolution and development of all achievements in terms of four relationships based on the real-world evaluation system. Building upon the foundation of the perfect evaluation model, we put forth four-round rules to eliminate non-significant achievements by utilizing four relationships. This process allows us to establish a pragmatic evaluation model that effectively captures the essential achievements, serving as a curated collection of the top N achievements within a specific field during a specific timeframe. We present a case study on the top 100 Chip achievements to demonstrate the effectiveness of our science and technology evaluatology. The case study highlights its practical application and efficacy in identifying significant achievements and researchers that otherwise cannot be identified by using bibliometrics

Simultaneous Removal of Cu²⁺, Cd²⁺ and Pb²⁺ by Modified Wheat Straw Biochar from Aqueous Solution: Preparation, Characterization and Adsorption Mechanism

As an eco-friendly and efficient adsorbent for removal of potential toxic metals from aqueous solution, biochar has received widespread attention. In the present study, wheat straw biochar (BC) and corresponding modified biochar (HNC) were used to remove Cu2+, Cd2+ and Pb2+ from an aqueous solution. The influence of the environment factors on metals adsorption and adsorption mechanism were discussed in detail. The results showed that the HNC had porous structures and owned ample functional groups (−OH, −COOH and C−N groups) compared with the BC. In the single system, the adsorption capacities of HNC for Cu2+, Cd2+ and Pb2+ at a pH of 5.5 were 18.36, 22.83 and 49.38 mg/g, which were 76.89%, 164.36% and 22.75% higher than that of the BC, respectively. In addition, the adsorption process of Cu2+ and Cd2+ on BC and HNC fitted to the Langmuir isotherm model and pseudo-second-order kinetics, but the adsorption of Pb2+ on BC and HNC fitted to the Langmuir isotherm model and pseudo-first-order kinetics. Adsorption isotherms indicated that the adsorption of Cu2+, Cd2+ and Pb2+ by BC and HNC was a spontaneous endothermic process. The competitive adsorption of mixed metal ions (Cu2+, Cd2+ and Pb2+) revealed that HNC was more preferential to adsorb Cu2+ compared with Cd2+ and Pb2+. Furthermore, Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy analyses revealed that the main adsorption mechanisms were surface complexation and precipitation, and the adsorbed Cu2+, Cd2+ and Pb2+ on HNC mainly exist as CuO, Cd(OH)2, Pb3O4 and Pb(OH)2

Simultaneous Removal of Cu2+, Cd2+ and Pb2+ by Modified Wheat Straw Biochar from Aqueous Solution: Preparation, Characterization and Adsorption Mechanism

As an eco-friendly and efficient adsorbent for removal of potential toxic metals from aqueous solution, biochar has received widespread attention. In the present study, wheat straw biochar (BC) and corresponding modified biochar (HNC) were used to remove Cu2+, Cd2+ and Pb2+ from an aqueous solution. The influence of the environment factors on metals adsorption and adsorption mechanism were discussed in detail. The results showed that the HNC had porous structures and owned ample functional groups (−OH, −COOH and C−N groups) compared with the BC. In the single system, the adsorption capacities of HNC for Cu2+, Cd2+ and Pb2+ at a pH of 5.5 were 18.36, 22.83 and 49.38 mg/g, which were 76.89%, 164.36% and 22.75% higher than that of the BC, respectively. In addition, the adsorption process of Cu2+ and Cd2+ on BC and HNC fitted to the Langmuir isotherm model and pseudo-second-order kinetics, but the adsorption of Pb2+ on BC and HNC fitted to the Langmuir isotherm model and pseudo-first-order kinetics. Adsorption isotherms indicated that the adsorption of Cu2+, Cd2+ and Pb2+ by BC and HNC was a spontaneous endothermic process. The competitive adsorption of mixed metal ions (Cu2+, Cd2+ and Pb2+) revealed that HNC was more preferential to adsorb Cu2+ compared with Cd2+ and Pb2+. Furthermore, Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy analyses revealed that the main adsorption mechanisms were surface complexation and precipitation, and the adsorbed Cu2+, Cd2+ and Pb2+ on HNC mainly exist as CuO, Cd(OH)2, Pb3O4 and Pb(OH)2.</jats:p