Miracle-dz/UniFAR-FTUs huggingface dataset:
Textures for coding bathymetric data quality sectors on electronic navigational chart displays: design and evaluation

ABSTRACT The Zones of Confidence (ZOC) is a composite data quality indicator used in Electronic Navigational Charts. Accident reports show that failing to account for chart data quality can result in maritime accidents and loss of life. ZOC overlays are intended to help mariners in identifying potential seafloor hazards and in plotting routes safe for the vessel, but a major concern with the ZOC concept has been the utilized symbology with glyphs consisting of stars. Due to its recognized deficiencies, star-symbology has been rejected for use with the successor of ZOC, the Quality of Bathymetric Data (QoBD). This work presents a research effort toward a new QoBD representation. We define the requirements for the new coding scheme to be effective and propose two texture schemes incorporating countable elements, one consisting of lines and one of dot clusters. For comparison, we developed three alternative, color-based, coding schemes based on ideas previously expressed in the maritime community. Lastly, we present the design, dissemination, and results of an online user survey carried out to evaluate the five coding schemes. The survey results demonstrate that the proposed textures are the most preferred coding schemes among survey respondents.
Neighborhood Land Uses as Predictors of the Upward Mobility of Poor Youth

: Land uses within the neighborhood where poor youths reside have important effects on their development and therefore may predict their welfare as adults. Mean household incomes as adults and teenage birth rates of youth growing up in poor households are predicted based upon the land uses within the neighborhood they occupied as teenagers. Controlling for an extensive set of neighborhood socioeconomic and demographic variables, land uses are found to add to our understanding of the characteristics of places with upward mobility. Differences in land use between white and minority youth neighborhoods are also found to explain racial gaps in adult outcomes.
An Iterative Two-Stage Multifidelity Optimization Algorithm for Computationally Expensive Problems

Engineering design optimization often involves use of numerical simulations to assess the performance of candidate designs. The simulations for computing high-fidelity (HF) performance estimates, such as finite element analysis or computational fluid dynamics, are typically computationally expensive. In some cases, it may also be possible to run an alternate or cheaper version of the simulation (through, e.g., use of a coarse mesh) to yield a low-fidelity (LF) performance estimate. Multifidelity optimization refers to the class of methods that aim to manage LF and HF evaluations efficiently to optimize computationally expensive problems within a limited computing budget. Among the prominent existing multifidelity approaches, some of them depend on a sufficiently dense a priori sampling; while others use unidirectional information exchange from LF to HF; both of which lead to a possibility of spending evaluation budget on unpromising search regions. This article proposes an improved multifidelity approach using an iterative, two-stage scheme (MFITS). It uses the collective information from the previously evaluated designs to determine a sampling neighborhood for LF evaluations. These samples are, in turn, used for building a co-kriging surrogate model that is then searched globally to identify a good candidate for HF evaluation. By restricting the LF sampling neighborhood, the computational budget can be used more efficiently, as the search is focused on regions that have historically produced good quality solutions. Numerical experiments and benchmarking are conducted on two suites of test problems and two practical design optimization problems to demonstrate the efficacy of MFITS.
Network Structure Engineering of Organosilica Membranes for Enhanced CO2 Capture Performance

The membrane separation process for targeted CO2 capture application has attracted much attention due to the significant advantages of saving energy and reducing consumption. High-performance separation membranes are a key factor in the membrane separation system. In the present study, we conducted a detailed examination of the effect of calcination temperatures on the network structures of organosilica membranes. Bis(triethoxysilyl)acetylene (BTESA) was selected as a precursor for membrane fabrication via the sol-gel strategy. Calcination temperatures affected the silanol density and the membrane pore size, which was evidenced by the characterization of FT-IR, TG, N2 sorption, and molecular size dependent gas permeance. BTESA membrane fabricated at 500 °C showed a loose structure attributed to the decomposed acetylene bridges and featured an ultrahigh CO2 permeance around 15,531 GPU, but low CO2/N2 selectivity of 3.8. BTESA membrane calcined at 100 °C exhibited satisfactory CO2 permeance of 3434 GPU and the CO2/N2 selectivity of 22, displaying great potential for practical CO2 capture application.
Impact of the Use of Intelligent Wells on the Evaluation of Oilfield Development and Production Strategy

Intelligent wells are widely used around the world and they have the potential to significantly improve oil production or control water production of wells and fields. However, in many cases, the definition of the number and position of valves is still made considering only the well without evaluating if the decision to use them can change other important aspects of the production strategy. This article presents a study to evaluate some relevant aspects of the inclusion of intelligent wells in a more global study of production strategy selection. Such inclusion is an important step in the precise evaluation of the benefits of intelligent valves. The methodology comprises the economic optimization of a production strategy under different limits of platform flow capacity, the optimization of the number and position of valves (intelligent wells), including and excluding conventional well operation. This study was applied to the UNISIM-I-D benchmark case, starting with a previously optimized production strategy, regarding type, number and position of wells, well-opening sequence and platform flow capacity in 9 different geological scenarios. The optimization methodology uses a complex workflow to test different strategy alternatives using a genetic algorithm and a methodology to optimize the number and position of valves. We showed that the use of intelligent wells can significantly alter the water flow capacity and the operational design of wells. However, for this specific case, the use of intelligent wells was not able to modify oil production and water injection flow capacity. Intelligent well application was viable for 7 out of 9 geological scenarios with the number of valves varying from 1 to 14. The intelligent-well application improved the total NPV from 0% to 1.5%. The platform water flow capacity could be reduced by at least 30% if intelligent valves were implemented. These results are quite different when a less precise optimization methodology is applied, yielding an overestimation of an intelligent well. To conclude, the application of intelligent wells was viable for most of the scenarios. Although intelligent wells present low impact on NPV, they can modify the design of platform capacity significantly. This fact suggests that the optimization of intelligent wells must be combined with the optimization of the platform water flow capacity and the conventional well operation optimization. This work provides important information for reservoir engineers who use reservoir simulation to optimize production strategy. Currently, in many cases, intelligent wells are only evaluated after the selection of the platform design. We have proved that the combined optimization can yield a different production strategy design. In addition, we have also proved that the evaluation of intelligent wells viability without an adequate optimization of conventional well operation overestimated the number and the value of valves.
Oncolytic Herpes Simplex Virus Type 1 Induces Immunogenic Cell Death Resulting in Maturation of BDCA-1+ Myeloid Dendritic Cells

Recently, a paradigm shift has been established for oncolytic viruses (OVs) as it was shown that the immune system plays an important role in the specific killing of tumor cells by OVs. OVs have the intrinsic capacity to provide the right signals to trigger anti-tumor immune responses, on the one hand by delivering virus-derived innate signals and on the other hand by inducing immunogenic cell death (ICD), which is accompanied by the release of various damage-associated molecules from infected tumor cells. Here, we determined the ICD-inducing capacity of Talimogene laherparepvec (T-VEC), a herpes simplex virus type 1 based OV, and benchmarked this to other previously described ICD (e.g., doxorubicin) and non-ICD inducing agents (cisplatin). Furthermore, we studied the capability of T-VEC to induce the maturation of human BDCA-1+ myeloid dendritic cells (myDCs). We found that T-VEC treatment exerts direct and indirect anti-tumor effects as it induces tumor cell death that coincides with the release of hallmark mediators of ICD, while simultaneously contributing to the maturation of BDCA-1+ myDCs. These results unequivocally cement OVs in the category of cancer immunotherapy.
Heights of Cb clouds around Calcutta airport – Diurnal and Seasonal variations

Based on radar data collected at the Meteorological Office, Calcutta airport during the years 1977-86, a study has been made on cumulonimbus (Cb) clouds with top heights at 6 km or more developed over an area of 2,80,000 sq. km around the said airport. The seasonal, monthwise and hourly distribution of the total number of Cb cell and their top heights, number of days with Cb, Cb cells treat reached tropopause height and the preferred places of formation of high Cb clouds have been examined and discussed and the salient feature have been summarised.
Transcriptome Analysis of the Cf-13-Mediated Hypersensitive Response of Tomato to Cladosporium fulvum Infection

Tomato leaf mold disease caused by Cladosporium fulvum (C. fulvum) is one of the most common diseases affecting greenhouse tomato production. Cf proteins can recognize corresponding AVR proteins produced by C. fulvum, and Cf genes are associated with leaf mold resistance. Given that there are many physiological races of C. fulvum and that these races rapidly mutate, resistance to common Cf genes (such as Cf-2, Cf-4, Cf-5, and Cf-9) has decreased. In the field, Ont7813 plants (carrying the Cf-13 gene) show effective resistance to C. fulvum; thus, these plants could be used as new, disease-resistant materials. To explore the mechanism of the Cf-13-mediated resistance response, transcriptome sequencing was performed on three replicates each of Ont7813 (Cf-13) and Moneymaker (MM; carrying the Cf-0 gene) at 0, 9, and 15 days after inoculation (dai) for a total of 18 samples. In total, 943 genes were differentially expressed, specifically in the Ont7813 response process as compared to the Moneymaker response process. Gene ontology (GO) classification of these 943 differentially expressed genes (DEGs) showed that GO terms, including “hydrogen peroxide metabolic process (GO_Process)”, “secondary active transmembrane transporter activity (GO_Function)”, and “mismatch repair complex (GO_Component)”, which were the same as 11 other GO terms, were significantly enriched. An analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that many key regulatory genes of the Cf-13-mediated resistance response processes were involved in the “plant hormone signal transduction” pathway, the “plant–pathogen interaction” pathway, and the “MAPK signaling pathway–plant” pathway. Moreover, during C. fulvum infection, jasmonic acid (JA) and salicylic acid (SA) contents significantly increased in Ont7813 at the early stage. These results lay a vital foundation for further understanding the molecular mechanism of the Cf-13 gene in response to C. fulvum infection.
A Review of Metabolomic Profiling in Rheumatoid Arthritis: Bringing New Insights in Disease Pathogenesis, Treatment and Comorbidities

Metabolomic analysis provides a wealth of information that can be predictive of distinctive phenotypes of pathogenic processes and has been applied to better understand disease development. Rheumatoid arthritis (RA) is an autoimmune disease with the establishment of chronic synovial inflammation that affects joints and peripheral tissues such as skeletal muscle and bone. There is a lack of useful disease biomarkers to track disease activity, drug response and follow-up in RA. In this review, we describe potential metabolic biomarkers that might be helpful in the study of RA pathogenesis, drug response and risk of comorbidities. TMAO (choline and trimethylamine oxide) and TCA (tricarboxylic acid) cycle products have been suggested to modulate metabolic profiles during the early stages of RA and are present systemically, which is a relevant characteristic for biomarkers. Moreover, the analysis of lipids such as cholesterol, FFAs and PUFAs may provide important information before disease onset to predict disease activity and treatment response. Regarding therapeutics, TNF inhibitors may increase the levels of tryptophan, valine, lysine, creatinine and alanine, whereas JAK/STAT inhibitors may modulate exclusively fatty acids. These observations indicate that different disease modifying antirheumatic drugs have specific metabolic profiles and can reveal differences between responders and non-responders. In terms of comorbidities, physical impairment represented by higher fatigue scores and muscle wasting has been associated with an increase in urea cycle, FFAs, tocopherols and BCAAs. In conclusion, synovial fluid, blood and urine samples from RA patients seem to provide critical information about the metabolic profile related to drug response, disease activity and comorbidities.
The dynamic process of dynamic modelling: the cocaine epidemic in the United States of America

In the present article, the authors review several recent dynamic models of the current cocaine epidemic in the United States of America (both uncontrolled and optimally controlled), which differentiate between two levels of use (“light” and “heavy”). Even though all the models have their origin in a study carried out at the RAND Corporation's Drug Policy Research Center in the early 1990s, each has been developed by extending or refining another. In addition to pointing to interesting policy conclusions drawn from the analysis of those models, the authors also demonstrate that the development of dynamic models of illicit drug consumption is itself a dynamic process where subsequent refinements lead to increased quality and reliability of the resulting policy conclusions.
Simulated Forest Immersion Therapy: Methods Development

Shinrin-yoku, forest bathing, may provide relief from chronic and breakthrough pain in patients with axial spondyloarthritis and improve immune function through increasing NK cell numbers and activity and their downstream effectors, perforin and granulysin, after chemo- or radiation therapy in breast and prostate cancer patients. The aim of this paper is to describe the study protocol for a simulated forest immersion therapy using virtual reality and atomized phytoncides, volatile organic compounds found in forested areas designed to effect positive change for these two patient populations. The setting, including the room set up and samples with inclusion/exclusion specific to this type of intervention, is outlined. Measures and calibration procedures pertinent to determining the feasibility of simulated forest immersion therapy are presented and include: ambient and surface room temperatures and relative humidity in real time, ambient ultrafine particulate matter, ambient droplet measurement that coincides with volatile organic compounds, specific phytoncides, and virtual reality and atomization of phytoncide set up. Particular lessons learned while training and setting up the equipment are presented. Simulated forest immersion therapy is possible with attention to detail during this early phase when development of methods, equipment testing, and feasibility in deploying the intervention become operational. The expected outcome of the development of the methods for this study is the creation of a standardized approach to simulating forest therapy in a controlled laboratory space.
Large-Scale Production of Size-Adjusted β-Cell Spheroids in a Fully Controlled Stirred-Tank Reactor

For β-cell replacement therapies, one challenge is the manufacturing of enough β-cells (Edmonton protocol for islet transplantation requires 0.5–1 × 106 islet equivalents). To maintain their functionality, β-cells should be manufactured as 3D constructs, known as spheroids. In this study, we investigated whether β-cell spheroid manufacturing can be addressed by a stirred-tank bioreactor (STR) process. STRs are fully controlled bioreactor systems, which allow the establishment of robust, larger-scale manufacturing processes. Using the INS-1 β-cell line as a model for process development, we investigated the dynamic agglomeration of β-cells to determine minimal seeding densities, spheroid strength, and the influence of turbulent shear stress. We established a correlation to exploit shear forces within the turbulent flow regime, in order to generate spheroids of a defined size, and to predict the spheroid size in an STR by using the determined spheroid strength. Finally, we transferred the dynamic agglomeration process from shaking flasks to a fully controlled and monitored STR, and tested the influence of three different stirrer types on spheroid formation. We achieved the shear stress-guided production of up to 22 × 106 ± 2 × 106 viable and functional β-cell spheroids per liter of culture medium, which is sufficient for β-cell therapy applications.
50-nm vertical sidewall transistors with high channel doping concentrations

Vertical MOSFETs have been proposed in the roadmap of semiconductors as a candidate for sub 100 nm CMOS technologies. A process flow using sidewall gates and implantations instead of multiple layer depositions reduces process complexity and offers better CMOS compatibility. High doping concentrations in the channel are needed for sub 100 nm devices. Especially for vertical transistors the uniform channel doping is more critical than for a planar technology, where optimized profiles can be easier implemented. Therefore, we investigated for the first time vertical MOSFETs with high channel doping concentration up to 1*10/sup 19/ cm/sup -3/ and channel lengths down to 50 nm. The impact of the high doping levels on threshold voltage and on tunneling currents is discussed. Finally, by using slight process modifications first results on vertical double gate MOSFETs will be presented, which in principle can operate with an undoped channel region.
University Technology Transfer Through Innovation Incubator: A Case Study

This paper examines innovation incubators connected with Innovation University in the United States. The main goal of innovation incubators is commercializing new technologies and economic development. The research methodologies adopted in this research study are literature reviews and a case study, from Stanford University. Stanford University has been a leader in technology transfer, commercializing new technology and economic development since 1970.The paper concludes that innovation incubators represent a strategy for commercializing new technologies, and leading to sustainable economic growth. The case study covers the period from 1970 to 2010.
Impact of Autonomous Vehicles on Traffic Management: Case of Dynamic Lane Reversal

As the future of autonomous vehicles (AVs) becomes more certain, transport network managers may seek ways to reinvent elements of the traffic network to improve efficiency. One possibility is dynamic lane reversal, in which the network operator makes use of AV communications and behavior to change the direction of flow on a road link at smaller time intervals than would be possible with human drivers. Although there is much research into the mechanical details of AVs, this study motivates the need for future research by focusing on a planning application in which AVs are already present. A novel extension to an established system optimal dynamic traffic assignment model based on the cell transmission model was examined. The model determined the optimal lane configuration at small space–time intervals. Results demonstrate the model on a single link and a grid network and explore the dynamic demand scenarios that are most conducive to increasing system efficiency with dynamic lane reversal.
Training Aircraft Selection of the Vietnam People's Air Force Using a Hybrid BWM-Fuzzy TOPSIS Method

Demands that the Vietnam People's Air Force (VPAF) have new modern training aircraft have been growing recently. Although other training aircraft such as the Yak-52 and L-39 have performed well for decades, they are no longer able to perform the full range of training tasks required of them due to an increasing technology gap. In 2016, the United States government lifted a decade-long ban on lethal arms sales for Vietnam. This has created opportunities for Vietnam to access a variety of weapons suppliers from many countries that have a strong, global defence industry. However, one of the most difficult decisions the VPAF must make concerns the type, configuration, and capabilities of future training aircraft. This study therefore proposes a Multi-Criteria Decision Making (MCDM) model by combining the Best Worst Method (BWM) and a Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) to choose a modern training aircraft that can replicate the characteristics of several fourth-generation or better fighter planes, with the fifth-generation fighters also being able to perform light-attack and reconnaissance duties for VPAF. The case study employing the hybrid BWM-Fuzzy TOPSIS method reveals that the Yak-130 training aircraft is the best selection for VPAF. To validate the robustness of the proposed framework, sensitivity analysis has been conducted with the result compared to Analytic Hierarchy Process (AHP).
An Investigation on Developing a Drilling Burr Prediction Model

: Presence of burr after a manufacturing process is a potential problem. Prediction of burr size in machining plays an important role in process planning. The present work deals with the study and development of a burr height prediction model during machining aluminium alloy, using Response Surface Methodology (RSM). Drilling parameter such as cutting velocity, feed rate and drill diameter are considered in conducting experiments in a radial drilling machine to find out height of burr formed in aluminium alloy flats. By performing ANOVA, it is found that drill diameter is the most significant factor to influence formation of burr height. Combination of process parameters giving small burr height is found out within the experimental constraints. Three-dimensional surface and contour plots show the variation in burr height with different process parameters. A comparison of modeled and experimental results corresponding to a low burr height shows an error of less than 3%.