Miracle-dz/UniFAR-FTUs huggingface dataset:
APTES-Based Silica Nanoparticles as a Potential Modifier for the Selective Sequestration of CO2 Gas Molecules

In this work, we have described the characterization of hybrid silica nanoparticles of 50 nm size, showing outstanding size homogeneity, a large surface area, and remarkable CO2 sorption/desorption capabilities. A wide battery of techniques was conducted ranging from spectroscopies such as: UV-Vis and IR, to microscopies (SEM, AFM) and CO2 sorption/desorption isotherms, thus with the purpose of the full characterization of the material. The bare SiO2 (50 nm) nanoparticles modified with 3-aminopropyl (triethoxysilane), APTES@SiO2 (50 nm), show a remarkable CO2 sequestration enhancement compared to the pristine material (0.57 vs. 0.80 mmol/g respectively at 50 °C). Furthermore, when comparing them to their 200 nm size counterparts (SiO2 (200 nm) and APTES@SiO2 (200 nm)), there is a marked CO2 capture increment as a consequence of their significantly larger micropore volume (0.25 cm3/g). Additionally, ideal absorbed solution theory (IAST) was conducted to determine the CO2/N2 selectivity at 25 and 50 °C of the four materials of study, which turned out to be >70, being in the range of performance of the most efficient microporous materials reported to date, even surpassing those based on silica.
IS JAPAN BACK? MEASURING NATIONALISM AND MILITARY ASSERTIVENESS IN ASIA'S OTHER GREAT POWER

Abstract Observers of East Asia frequently claim that Japanese nationalism is on the rise, and that Tokyo is abandoning its longtime military restraint. To determine whether these trends are indeed occurring, we define and measure Japan's nationalism and military assertiveness; we measure whether they are rising relative to Japan in the past, and relative to seven other countries. Drawing from social identity theory, we distinguish between “nationalism” and a more benign “patriotism. ” We find in Japan (1) strong patriotism that is stable over time, and no evidence of rising nationalism. Furthermore we find that (2) military assertiveness remains generally low, but it has risen in terms of decreased institutional constraints and peacekeeping activities. Our findings have important implications for academic debates about nationalism and Japanese security policy, and for policy debates about a nascent balancing effort against China.
Green IT: Reduce Your Information System's Environmental Impact While Adding to the Bottom Line

Reduce the environmental and budgetary impact of your IT infrastructure This groundbreaking work offers a complete roadmap for integrating environmentally sound techniques and technologies into your Information Systems architecture. Green IT explains how to adopt a business-driven green initiative and provides a detailed implementation plan. You will find strategies for reducing power needs, procuring energy from alternative sources, utilizing virtualization technologies, and managing sustainable development. Case studies highlighting successful green IT projects at major organizations are included. Keep your IT department and your organization in the green--both environmentally and financially--with help from this comprehensive guide. Work within current global initiatives and standards for e-waste Minimize power usage and use alternative cooling methods in your datacenter Transition your office into a paperless environment Equip your organization with green hardware, including EPEAT-, RoHS-, and ENERGY STAR-certified machines Implement efficient datacenter design in terms of energy consumption, cooling, server configuration, consolidation, cabling, redundancy, and more Virtualize servers and storage using the latest technologies from VMware, Microsoft, Compellent, Incipient, and others Measure existing datacenter efficiency using current metrics, and track progress with Business Intelligence tools Establish a green supply chain Explore the Software as a Service (SaaS) model Manage ongoing compliance and sustainable growth Table of contents Part I: Trends and Reasons to Go Green Chapter 1. Overview and Issues Chapter 2. Current Initiatives and Standards Part II: Consumption Issues Chapter 3. Minimizing power usage Chapter 4. Cooling Part III: What You Can Do Chapter 5. Changing the Way We Work Chapter 6. Going Paperless Chapter 7. Recycling Chapter 8. Hardware Considerations Part IV: Case Studies Chapter 9. Technology Businesses Chapter 10. Other Organizations Part V: The Greening Process Chapter 11. Datacenter Design and Redesign Chapter 12. Virtualization Chapter 13. Greening Your Information Systems Chapter 14. Staying Green
Microstructural and Texturizing Properties of Partially Pectin-Depleted Cell Wall Material: The Role of Botanical Origin and High-Pressure Homogenization

In the current study, the texturizing properties of partially pectin-depleted cell wall material (CWM) of apple, carrot, onion and pumpkin, and the potential of functionalization by high-pressure homogenization (HPH) were addressed. This partially pectin-depleted CWM was obtained as the unextractable fraction after acid pectin extraction (AcUF) on the alcohol-insoluble residue. Chemical analysis was performed to gain insight into the polysaccharide composition of the AcUF. The microstructural and functional properties of the AcUF in suspension were studied before HPH and after HPH at 20 and 80 MPa. Before HPH, even after the pectin extraction, the particles showed a cell-like morphology and occurred separately in the apple, onion and pumpkin AcUF and in a clustered manner in the carrot AcUF. The extent of disruption by the HPH treatments at 20 and 80 MPa was dependent on the botanical origin. Only for the onion and pumpkin AcUF, the water binding capacity was increased by HPH. Before HPH, the texturizing potential of the AcUFs was greatly varying between the different matrices. Whereas HPH improved the texturizing potential of the pumpkin AcUF, no effect and even a decrease was observed for the onion AcUF and the apple and carrot AcUF, respectively.
Differential evolution algorithm-based multiple-factor optimization methods for data assimilation

The methods of searching for optimized parameters have substantial effects on the forecast accuracy of ensemble data assimilation systems. The selection of these factors is usually performed using trial-and-error methods, and poor parameterizations may lead to filter divergence. Combined with the local ensemble transform Kalman filtering method (LETKF), a technique for an automated search of the best configuration (parameters) of a data assimilation system is proposed. To obtain better assimilation, a differential evolution (DE) algorithm-based multiple-factor parameterization method results in the corresponding circumstances. By combining with fast-searching DE algorithms, we may retrieve the most ideal parameter combinations. Several numerical experiments performed with the Lorenz-96 model show that new methods performed better than the original one-parameter optimization methods. As the basis of DE methods, the best combinations of the local radius and the covariance inflation parameter, which can guarantee the best DA performances in the corresponding circumstances, are retrieved. It is found that the new method is capable of outperforming previous search algorithms under both perfect and imperfect model scenarios, and the calculation cost in Lorenz-96 model is lower. However, how to apply the new proposed method to more complex atmospheric or land surface models requires further verification.
SQL and NoSQL Databases in the Context of Industry 4.0

The data-oriented paradigm has proven to be fundamental for the technological transformation process that characterizes Industry 4.0 (I4.0) so that Big Data & Analytics is considered a technological pillar of this process. The literature reports a series of system architecture proposals that seek to implement the so-called Smart Factory, which is primarily data-driven. Many of these proposals treat data storage solutions as mere entities that support the architecture's functionalities. However, choosing which logical data model to use can significantly affect the performance of the architecture. This work identifies the advantages and disadvantages of relational (SQL) and non-relational (NoSQL) data models for I4.0, taking into account the nature of the data in this process. The characterization of data in the context of I4.0 is based on the five dimensions of Big Data and a standardized format for representing information of assets in the virtual world, the Asset Administration Shell. This work allows identifying appropriate transactional properties and logical data models according to the volume, variety, velocity, veracity, and value of the data. In this way, it is possible to describe the suitability of SQL and NoSQL databases for different scenarios within I4.0.
Synthesis and Antiproliferative Evaluation of 3-Chloroazetidin-2-ones with Antimitotic Activity: Heterocyclic Bridged Analogues of Combretastatin A-4

Antimitotic drugs that target tubulin are among the most widely used chemotherapeutic agents; however, the development of multidrug resistance has limited their clinical activity. We report the synthesis and biological properties of a series of novel 3-chloro-β-lactams and 3,3-dichloro-β-lactams (2-azetidinones) that are structurally related to the tubulin polymerisation inhibitor and vascular targeting agent, Combretastatin A-4. These compounds were evaluated as potential tubulin polymerisation inhibitors and for their antiproliferative effects in breast cancer cells. A number of the compounds showed potent activity in MCF-7 breast cancer cells, e.g., compound 10n (3-chloro-4-(3-hydroxy-4-methoxy-phenyl)-1-(3,4,5-trimethoxyphenyl)azetidin-2-one) and compound 11n (3,3-dichloro-4-(3-hydroxy-4-methoxyphenyl)-1-(3,4,5-trimethoxyphenyl)-azetidin-2-one), with IC50 values of 17 and 31 nM, respectively, and displayed comparable cellular effects to those of Combretastatin A-4. Compound 10n demonstrated minimal cytotoxicity against non-tumorigenic HEK-293T cells and inhibited the in vitro polymerisation of tubulin with significant G2/M phase cell cycle arrest. Immunofluorescence staining of MCF-7 cells confirmed that β-lactam 10n caused a mitotic catastrophe by targeting tubulin. In addition, compound 10n promoted apoptosis by regulating the expression of pro-apoptotic protein BAX and anti-apoptotic proteins Bcl-2 and Mcl-1. Molecular docking was used to explore the potential molecular interactions between novel 3-chloro-β-lactams and the amino acid residues of the colchicine binding active site cavity of β-tubulin. Collectively, these results suggest that 3-chloro-2-azetidinones, such as compound 10n, could be promising lead compounds for further clinical anti-cancer drug development.
Open Application of Statistical and Machine Learning Models to Explore the Impact of Environmental Exposures on Health and Disease: An Asthma Use Case

ICEES (Integrated Clinical and Environmental Exposures Service) provides a disease-agnostic, regulatory-compliant approach for openly exposing and analyzing clinical data that have been integrated at the patient level with environmental exposures data. ICEES is equipped with basic features to support exploratory analysis using statistical approaches, such as bivariate chi-square tests. We recently developed a method for using ICEES to generate multivariate tables for subsequent application of machine learning and statistical models. The objective of the present study was to use this approach to identify predictors of asthma exacerbations through the application of three multivariate methods: conditional random forest, conditional tree, and generalized linear model. Among seven potential predictor variables, we found five to be of significant importance using both conditional random forest and conditional tree: prednisone, race, airborne particulate exposure, obesity, and sex. The conditional tree method additionally identified several significant two-way and three-way interactions among the same variables. When we applied a generalized linear model, we identified four significant predictor variables, namely prednisone, race, airborne particulate exposure, and obesity. When ranked in order by effect size, the results were in agreement with the results from the conditional random forest and conditional tree methods as well as the published literature. Our results suggest that the open multivariate analytic capabilities provided by ICEES are valid in the context of an asthma use case and likely will have broad value in advancing open research in environmental and public health.
Computer-aided design of graphene and 2D materials synthesis via magnetic inductive heating of 11 transition metals

We performed numerical simulations to determine the effect of the most influential operating parameters on the performance of a radio frequency (RF) induction-heating system in which RF magnetic fields inductively heat metal foils to grow graphene. The thermal efficiency of the system depends on the geometry as well as on the materials’ electrical conductivity and skin depth. The process is simulated under specific graphene and two-dimensional (2D) materials growth conditions using finite elements software in order to predict the transient temperature and magnetic field distribution during standard graphene and 2D materials growth conditions. The proposed model considers different coil Helmholtz-like geometries and 11 metal foils, including Ag, Au, Cu, Ni, Co, Pd, Pt, Rh, Ir, Mo, and W. In each case, an optimal window of process variables ensuring a temperature range of 1035 °C–1084 °C or 700 °C–750 °C suitable for graphene and MoS2 growth, respectively, was found. Temperature gradients calculated from the simulated profiles between the edge and the center of the substrate showed a thermal uniformity of less than ∼2% for coinage metals like Au, Ag, and Cu and up to 7% for Pd. Model validation was performed for graphene growth on copper. Due to its limited heat conductivity, good heating uniformity was obtained. As a consequence, full coverage of monolayer graphene on copper with few defects and a grain domain size of ∼2 µm was obtained. The substrate temperature reached ∼1035 °C from ambient after only ∼90 s, in excellent agreement with model predictions. This allows for improved process efficiency in terms of fast, localized, homogeneous, and precise heating with energy saving. Due to these advantages, inductive heating has great potential for large-scale and rapid manufacturing of graphene and 2D materials.
Applying numerical optimization to realistic high-lift design of transport aircraft – an overview of the aerodynamic design optimisation investigations within the EUROLIFT II project

The EUROLIFT II project, funded by the European Commission within the 6th framework programme, is dedicated to the investigation of transport aircraft in high-lift configuration. It covers both numerical and experimental studies, mainly targeted towards validation of CFD for maximum lift prediction of such configurations. It is a follow up to the EUROLIFT project of the 5th framework programme. While in the former project a simplified wing-body high-lift configuration was investigated, the new project focuses on a more realistic configuration including engine nacelles, pylons, tracks and brackets. Additionally the next step is undertaken coming from analysis to design. Since it is a main aim of the project to deal with realistic configurations, this design will be done at a Reynolds number comparable to flight conditions of a real transport aircraft. The design activity within the project is targeted towards an improvement of the aerodynamic properties of the DLR-F11 model already used in EUROLIFT. The investigations mainly focus on an increased take-off performance by modifying the trailing edge flap both in shape and position.
Hyperspectral Image Super-Resolution Based on Multiscale Mixed Attention Network Fusion

Hyperspectral images (HSIs) contain rich spectral information and have great application value. However, due to various hardware limitations, the spatial resolution of HSIs acquired by the sensor is low. HSI super-resolution (SR) attracts much attention to improve spatial quality. In this letter, a single HSI SR method based on network fusion is proposed. Our method includes the SR network part and fusion part. In the SR network part, we construct 3-D multiscale mixed attention networks (3-D-MSMANs) by cascading 3-D multiscale mixed attention block (3-D-MSMAB) to restore high-resolution HSIs. 3-D-MSMAB consists of the 3-D Res2net module and the mixed attention module. 3-D Res2net module is a simple and effective multiscale method. The mixed attention module is proposed by combining the first- and second-order statistics of features. In addition, we use the mutual learning loss between 3-D-MSMAN so that they can learn from each other. In the fusion part, the fusion module is designed to merge the output of each 3-D-MSMAN. Our method can achieve good results in both simulated and real SR experiments. Code is available at https://github.com/LYT-max/Mixed-Attention-for-HSI-SR.
Data Driven Transformer Thermal Model for Condition Monitoring

Condition monitoring of power transformers, which are key components of electrical power systems, is essential to identify incipient faults and avoid catastrophic failures. In this paper machine learning algorithms, i.e., nonlinear autoregressive neural networks and support vector machines, are proposed to model the transformer thermal behavior for the purpose of monitoring. The thermal models are developed based on the historical measurements from nine transformers comprised of two 180-MVA units, four 240-MVA units and three 1000-MVA units. The data consist of load profile, tap position, winding indicator temperature (WTI) measurement, ambient temperature, wind speed and solar radiation. The results are validated against field measurements, and it is clearly demonstrated that the alternative algorithms surpass the IEEE Annex G thermal model. An incipient thermal fault identification algorithm is then proposed and successfully used to identify an issue using measurements taken in the field. This algorithm could be used to alert the operator and plan intervention accordingly.
High Datarate Solutions for Next Generation Wireless Communication

Next generation wireless communication systems are essential components in order to increase the capacity of existing digital networks. Channel bandwidth as wide as several gigahertz is generally available at millimeter wave frequencies. However, it is a challenge to design and implement millimeter wave transceivers that can utilize such wideband effectively for the data transmission. In this thesis, the feasibility of high data rate baseband modulators and demodulators (modems) that support various digital modulation formats is investigated. Novel modem structures for OOK, D-QPSK/QPSK, 8-PSK and 16-QAM modulation are presented, and six proof-of-concept modems are designed and verified using these new structures. The work mentioned in this thesis constitutes an efficient portfolio of base- band modem solutions for different wireless communication applications. These modem solutions are optimized for high capacity based on the hardware components that exist today. These solutions may in principle support data rates of up to 100 Gbps, given enough bandwidth and more advanced semiconductor devices.
Examining TikTok’s Potential for Community-Engaged Digital Knowledge Mobilization With Equity-Seeking Groups

Social media is increasingly being leveraged by researchers to engage in public debates and rapidly disseminate research results to health care providers, health care users, policy makers, educators, and the general public. This paper contributes to the growing literature on the use of social media for digital knowledge mobilization, drawing particular attention to TikTok and its unique potential for collaborative knowledge mobilization with underserved communities who experience barriers to health care and health inequities (eg, equity-seeking groups). Setting the TikTok platform apart from other social media are the unique audiovisual video editing tools, together with an impactful algorithm, that make knowledge dissemination and exchange with large global audiences possible. As an example, we will discuss digital knowledge mobilization with trans and nonbinary (trans) communities, a population that experiences barriers to health care and is engaged in significant peer-to-peer health information sharing on the web. To demonstrate, analytics data from 13 selected TikTok videos on the topic of research on gender-affirming medicine (eg, hormonal therapy and surgeries) are presented to illustrate how knowledge is disseminated within the trans community via TikTok. Considerations for researchers planning to use TikTok for digital knowledge mobilization and other related community engagement with equity-seeking groups are also discussed. These include the limitations of TikTok analytics data for measuring knowledge mobilization, population-specific concerns related to community safety on social media, the spread of disinformation, barriers to internet access, and commercialization and intellectual property issues. This paper concludes that TikTok is an innovative social media platform that presents possibilities for achieving transformative, community-engaged knowledge mobilization among researchers, underserved health care users, and their health care providers, all of whom are necessary to achieve better health care and population health outcomes.
Measurement of technological progress through analysis of learning rates; the case of the manufacturing industry in Mexico

The development and advancement of a manufacturing industry relies on specialization changes over time with a shift from low-tech labor-intensive to high-tech capital-intensive industries. The stocks of knowledge and technological capabilities influence the technological progress of an industry. We analyzed the Mexican manufacturing subsectors and estimated their progress ratios through a linear and a cubic model integrated into the neoclassical production function. The purpose was to determine whether Mexico has moved from labor-intensive to capital-intensive industries, and which subsectors would perform better. Three main patterns of technological learning were found among various industries; a convex learning path with continuous forgetting or learning at the beginning but forgetting afterwards, a concave learning path with forgetting in the beginning but learning afterwards, and a concave learning path with continuous forgetting. To sustain industrial and economic growth, Mexico could prioritize the mid-low and mid-high-tech industries that show learning potentials, and adjust its technology policy structure to reverse the hightech industry performance. Policies to support the food industry are recommended as it has been very competitive and shown a high assimilation capacity.