Miracle-dz/UniFAR-FTUs huggingface dataset:
Arctic seals as tracers of environmental and ecological change

Knowledge of species trophic position (TP) is an essential component of ecosystem management. Determining TP from stable nitrogen isotopes (δ15N) in predators requires understanding how these tracers vary across environments and how they relate to predator isotope composition. We used two seal species as a model for determining TP across large spatial scales in the Arctic. δ15N in seawater nitrate (δ15NNO3) and seal muscle amino acids (δ15NAA) were determined to independently characterize the base of the food web and the TP of harp and ringed seals, demonstrating a direct link between δ15NNO3 and δ15NAA. Our results show that the spatial variation in δ15NAA in seals reflects the δ15NNO3 end members in Pacific vs. Atlantic waters. This study provides a reference for best practice on accurate comparison of TP in predators and as such, provides a framework to assess the impact of environmental and human‐induced changes on ecosystems at pan‐Arctic scales.
Scheduling pipe laying support vessels with non-anticipatory family setup times and intersections between sets of operations

In this paper, we deal with a problem that arises from the oil industry with the need to schedule a pipe laying support vessel fleet responsible for connecting oil wells to production platforms. We model it as an identical parallel machine scheduling problem, considering a particular case where jobs are composed of intersecting sets of operations and with operations partitioned into families. A non-anticipatory family setup time is incurred on three occasions: when a machine changes the execution of operations from one family to another, when the machine reaches its capacity, and before the first operation on each machine. These considerations, along with other scheduling features, make the problem more challenging and attractive to the scheduling literature. We propose three mathematical formulations to solve 72 generated instances, based on actual data, with up to 50 operations to schedule. Among the formulations, the Batch Scheduling presents better results with the smallest gaps, when compared to the best lower bounds obtained. This formulation considers a dispatching rule to sequence operations within batches, generating better solutions in 22 of the 24 largest instances, dominating the other formulations. A Brazilian oil company currently uses this approach in a production system for its tactical planning.
Exploring the Cognitive and Emotional Impact of Online Climate Change Videos on Viewers

Climate change is a significant challenge for the international community. A significant part of addressing this challenge involves informing people about climate change to try and change behavior. Organizations like Technology, Entertainment, and Design (TED) use social media as a means of disseminating information about the complexities of climate science. In this study, we investigate viewers’ responses to 50 TED videos associated with climate change that are posted on YouTube. We elucidate the opinions of both speakers and viewers through sentiment analysis of 59,023 comments and negative binomial regression techniques of viewers’ reactions. The most frequently mentioned keywords are emission, temperature, environment, nature, renewable energy, and economics. The top three emotions evoked by reviewer are trust, fear, and anticipation. The issue of economics is largely responsible for triggering these emotional responses.
Increased burning in a warming climate reduces carbon uptake in the Greater Yellowstone Ecosystem despite productivity gains

The effects of changing climate and disturbance on mountain forest carbon (C) stocks vary with tree species distributions and over elevational gradients. Warming can not only increase C uptake by stimulating productivity at high elevations but also enhance C release by increasing respiration and the frequency, intensity and size of wildfires. To understand the consequences of climate change for temperate mountain forests, we simulated interactions among climate, wildfire, tree species and their combined effects on regional C stocks in forests of the Greater Yellowstone Ecosystem, USA (GYE) with the LANDIS‐II landscape change model. Simulations used historical climate and future potential climate represented by downscaled projections from five general circulation models (GCMs) that bracket the range of variability under the representative concentration pathway (RCP) 8.5 emissions scenario. Total ecosystem C increased by 67% through 2100 in simulations with historical climate, and by 38%–69% with GCM climate. Differences in C uptake among GCMs resulted primarily from variation in area burned, not productivity. Warming increased productivity by extending the growing season, especially near upper tree line, but did not offset biomass losses to fire. By 2100, simulated area burned increased by 27%–215% under GCM climate, with the largest increases after 2050. With warming >3°C in mean annual temperature, the increased frequency of large fires reduced live C stocks by 4%–36% relative to the control, historical climate scenario. However, relative losses in total C were delayed under GCMs with large increases in summer precipitation and buffered by C retained in soils and the wood of fire‐killed trees. Increasing fire size limited seed dispersal, and reductions in soil moisture limited seedling establishment; both effects will likely constrain long‐term forest regeneration and C uptake. Synthesis. Forests in the GYE can maintain a C sink through the mid‐century in a warming climate but continued warming may cause the loss of forest area, live above‐ground biomass and, ultimately, ecosystem C. Future changes in C stocks in similar forests throughout western North America will depend on regional thresholds for extensive wildfire and forest regeneration and therefore, changes may occur earlier in drier regions.
Innovative Mobile Application for Measuring Big Data Maturity: Case of SMEs in Thailand

A Big Data maturity model (BDMM) is one of the key tools for Big Data assessment and monitoring, and a guideline for maximizing the usage and opportunity of Big Data in organizations. The development of a BDMM for SMEs is a new concept and is challenging in terms of development, application, and adoption. This article aims to create the novel online adaptive BDMM via responsive web application for SMEs. We develop the BDMM API and a responsive web application for easy access via mobile phone. We developed a model by analyzing the factors impacting the success of implementing Big Data Analytics (BDA) in SMEs based on literature reviews. The model was verified by conducting a survey of 180 SMEs in Thailand, interviewed against four extracted domains. Then, the scoring and classified levels for the model was developed through Latent Class Analysis (LCA) to depict four levels of each domain and four final maturity levels to create an adaptive model. As the experimental results with 33 users including executive officers, managers, IT and data analytic officers . The user acceptance for our mobile application using TAM indicates that executive officers group and non-executive group satisfied perceived usefulness, perceived ease of use, and intention to use factor. Use cases of the application include SMEs monitoring for their Big Data Analytics capability for improvement, and the Government Agency providing proper support on SMEs’ level of competency.
CONSTRUÇÃO E APLICAÇÃO DE UM ÍNDICE DE VULNERABILIDADE EM SAÚDE

Este trabalho teve por objetivo construir e aplicar um Índice de Vulnerabilidade em Saúde. Estudo metodológico de construção e validação de um indicador sintético de vulnerabilidade em saúde com dados provenientes do Censo Demográfico brasileiro. A validade dos indicadores foi examinada mediante escores numéricos atribuídos por avaliadores externos, utilizando-se a técnica Delphi de consenso. Foi realizada a agregação dos indicadores a partir da Análise de Componentes Principais (ACP). Por fim, foram construídos mapas na cidade de Aracaju, Sergipe, Brasil para a identificação das áreas de vulnerabilidade. A auto correlação espacial foi avaliada através do Índice de Moran Global e do Índice de Associação Espacial Local. Através do indicador de vulnerabilidade em saúde foi possível realizar o diagnóstico e monitoramento das condições de vulnerabilidade a que as pessoas estão expostas, pois demonstrou que é capaz de identificar as desigualdades sociais diante da heterogeneidade das áreas analisadas.A aplicação do Índice de Vulnerabilidade em Saúde no município analisado mostrou-se válida demonstrando a capacidade de aproximação com a realidade, sendo uma ferramenta útil para planejamento e avaliação das políticas de saúde no território e em outras regiões brasileiras.
A New Thermally-stable Insensitive High Explosive:N,N′-Bis(1,2,3,4-tetrazol-5-yl)-4,4′-diamino-2,2′,3,3′,5,5′,6,6′-octanitroazobenzene (BTeDAONAB)

A novel high performance explosive compound, N,N′-bis(1,2,3,4-tetrazol5-yl)-4,4′-diamino-2,2′,3,3′,5,5′,6,6′-octanitroazobenzene (BTeDAONAB), is introduced which is a thermally stable, insensitive high explosive. The presence of −NH2, −N=N− groups and the tetrazole ring simultaneously in the BTeDAONAB unit surprisingly increases its thermal stability. For the identification and characterization of BTeDAONAB, different analytical techniques have been used, including melting point, IR, 1H NMR, 13C NMR spectroscopy, elemental analysis, differential thermal analysis (DTA), thermogravimetry (TG), differential scanning calorimetry (DSC) and X-ray diffraction (XRD). The sensitivity and detonation properties of BTeDAONAB were compared with 2,4,6-triamino1,3,5-trinitrobenzene (TATB) and N,N′-bis(1,2,4-triazol-3-yl-)-4,4′-diamino2,2′,3,3′,5,5′,6,6′-octanitroazobenzene (BTDAONAB), two well-known, thermally stable, insensitive high explosives, as well as hexanitrostilbene (HNS). These studies show that BTeDAONAB has favorable thermal stability with high
Performance Analysis and Storage Management for Multiple Cloud Service Providers

: SAAS (Storage as a service) is a prominent cloud computing service that helps the cloud users to overcome the bounded resources and the storage expansion by advancing the devices. Data is outsourced by the encryption format due to the security and safety reasons so that the encrypted data generates lot of storage waste in cloud by complicating the data exchange among the authorized users. But here there are many challenges such as storage and managing encrypted data by duplication where as traditional duplication schemes in cloud always focus on application scenarios that are completely controlled by data owners or servers in cloud. Here the flexibility of satisfaction of different requests by data owners on level sensitivity of data. Present paper offers both reduplication scheme managing and access control of same time through multiple cloud service providers (CSP’s) for the evaluation and performance of comparison and implementation additionally security analysis.
Unsettled Care: Temporality, Subjectivity, and the Uneasy Ethics of Care

SUMMARY In this introduction, and indeed this special section, we explore care as a morally ambiguous and relationally unstable set of practices. By exploring care over longer temporal frames and across shifting subjectivities and intersubjectivities, we show how enactments of care are often unsettled by the transforming dynamics of relationships across time and often entail a multiplicity of competing affects and aspirations, such as hope and failure, love and resentment, pragmatism and utopianism, and connection and disconnection. We thus suggest an analytic approach to care that questions care as either morally suspect or morally virtuous and instead allows for the compromised, shifting, and ambiguous dimensions of care practices to take center stage.
Portlandite consumption by red ceramic waste due to alkali activation reaction

Abstract Red ceramics, due to the low compressive strength and high porosity, make it difficult to use it as an artificial aggregate in the production of mortars and concretes. However, since it has a silicon-rich composition, the ceramic material from blocks and tiles has been studied as a possible supplementary cementitious material in concrete and also as a raw material in alkali-activated binders. This paper aims to evaluate the content of calcium hydroxide (portlandite) fixed in the alkali activation reaction and the microstructure of red clay waste from construction and demolition waste (CDW) and hydrated lime mixtures, varying the atomic ratio between the silicon and the calcium. The results indicated that higher availability of lime is directly related to the content of hydrated compounds and its porosity. The increase in the silicon/calcium ratio resulted in a reduction of available lime content by 40% and an increase, in volume, of micropores by 7%.
Everyday (online) body politics of menstruation

ABSTRACT There has been a growing trend to resist mainstream body ideals via social media sites. From fat-acceptance, vulva-positive to menstrual-pride blogs, Tumblrs and Facebook groups, people use social media to question and challenge mainstream depictions of the female body. In this article, I look at social media culture and how the notions of the menstrual body are evolving online. I analyze these concerns with a case study based on a women-only closed Facebook group created to discuss issues around feminine health, sexuality, and wellbeing. I argue that by looking from the lens of everyday politics, it is possible to understand how political participation and social change can emerge through people’s everyday practices. My findings suggest that the private Facebook group serves three purposes. One, as a pedagogical space to address a gap in knowledge about the menstrual cycle and menstrual health. Two, as a platform to break the silence around menstruation and make it visible to the public. Three, as a tool for building a caring community among the participants. This study illustrates how social media is used for everyday body politics, contributing to changing attitudes, beliefs, and values in daily life.
PERFORMANCE EVALUATION OF MACHINE LEARNING ALGORITHMS IN FORECASTING WATER QUALITY INDICES: STUDY IN TAMILNADU WATER BODIES

Water quality prediction play an essential role in aqua environment management. The demand for accurate water quality prediction techniques for efficient water resources management. Currently, the Indian pollution control board has set up various monitoring stations to measure water quality frequently. However, the forecast for water quality is currently not being carried out. In this work, machine learning models have been implemented to predict the indices of water quality. The efficiency of logistic Linear regression and AdaBoostRegressor in the prediction of seven major water quality parameters were evaluated. The Tamil Nadu water quality dataset is used in this analysis. The parameters such as pH value, the quantity of oxygen dissolved, total coli form, B.D. O, electric conductivity, the quantity of phosphorus, and nitrate are considered. The assessed error-index value of the applied models showed that the AdaboostRegressor obtains a lesser error-index and it can consider being a more accurate model than the Linear regression model. The entire methodology proposed here is in the context of water quality is based on numerical analysis. While investigating the outcomes of the implemented machine learning models, it is demonstrated that they have nearly over-estimation properties. The proposed models are assessed using the metrics Mean Square Error and R 2 score the results reflect that AdaboostRegressor predicts the (Water Quality Indices) WQI rate with a Mean Square Error value of 0.8, and R 2 score rate is 0.41, whereas AdaBoostRegressor with a obtains Mean Square Error (MSE) rate as 0.74 and R 2 score rate as 0.44
Synergistic effect of multi walled carbon nanotubes and reduced graphene oxides in natural rubber for sensing application

Utilizing the electrical properties of polymer nanocomposites is an important strategy to develop high performance solvent sensors. Here we report the synergistic effect of multi walled carbon nanotubes (MWCNTs) and reduced graphene oxide (RGO) in regulating the sensitivity of the naturally occurring elastomer, natural rubber (NR). Composites were fabricated by dispersing CNTs alone and together with exfoliated RGO sheets (thermally reduced at temperatures of 200 and 600 °C ) in NR by a solution blending method. RGO exfoliation and the uniform distribution of fillers in the composites were studied by atomic force microscopy, Fourier transformation infrared spectroscopy, X-ray diffraction, transmission electron microscopy and Raman spectroscopy. The solvent sensitivity of the composite samples was noted from the sudden variation in electrical conductivity which was due to the breakdown of the filler networks during swelling in different solvents. It was found that the synergy between CNTs and RGO exfoliated at 200 °C imparts maximum sensitivity to NR in recognizing the usually used aromatic laboratory solvents. Mechanical and dynamic mechanical studies reveal efficient filler reinforcement, depending strongly on the nature of filler–elastomer interactions and supports the sensing mechanism. Such interactions were quantitatively determined using the Maier and Goritz model from Payne effect experiments. It is concluded that the polarity induced by RGO addition reduces the interactions between CNTs and ultimately results in the solvent sensitivity.
Magnetic Nanotubes: Synthesis, Properties, and Applications

This article presents a comprehensive review of recent progress of research dedicated to magnetic nanotubes (MNTs). The review mainly covers the recent achievements in the syntheses, properties, and applications of MNTs. After introducing the significance of MNTs and the magnetic characteristics of elements in the periodic table, the article starts with a brief overview of the existing fabrication pathways for MNTs followed by a detailed insight in selected methods. Properties and promising applications are discussed to guide future developments of MNTs. Special emphasis is put on the magnetic properties that are different from those of the corresponding bulk materials, such as obvious magnetic anisotropy, angle dependent magnetization and distinctive magnetization reversal. Finally, the paper concludes with perspectives and outlook for MNTs.
RHEOLOGICAL BEHAVIOR OF BENTONITE SUSPENSIONS

ABSTRACT The objective of this research is to investigate the concentration effect on the rheological properties of different bentonite suspensions. Experiments were performed with different bentonite type and concentrations and the results were evaluated by different rheological models (Bingham plastic, Power law, Sisco, Casson, Herchel Bulkley and Robertson Stiff) describing the flow of non-Newtonian fluids by mathematical equations in terms of shear rate and shear stress.
Multienvironment performance comparison of robot‐assisted indoor location estimation system

Many indoor location systems utilizing the fingerprinting technique require frequent signal map generation due to the dynamic properties of indoor environments. This time‐consuming task can be freed from human involvement by using various techniques. Thus, a robot‐aided signal map creator is implemented and its positioning performance as well as its mapping time is compared with a widely used human facilitated approach. For testing purposes, a low‐cost, low‐power, not off‐the‐shelf signal data collection robot is implemented. Its signal collection speed and location estimation precision is then compared with the manual method both in a small and large environment. The findings indicate that in the small environment, robot‐aided approach performs with 268‐cm error‐rate 70% of the time. On the other hand, the improved robot‐aided approach in large environment saves 52 min of labor‐intense legwork and achieves 303.5‐cm mean error‐rate.
InK: In-Kernel Key-Value Storage with Persistent Memory

Block-based storage devices exhibit different characteristics from main memory, and applications and systems have been optimized for a long time considering the characteristics in mind. However, emerging non-volatile memory technologies are about to change the situation. Persistent Memory (PM) provides a huge, persistent, and byte-addressable address space to the system, thereby enabling new opportunities for systems software. However, existing applications are usually apt to indirectly utilize PM as a storage device on top of file systems. This makes applications and file systems perform unnecessary operations and amplify I/O traffic, thereby under-utilizing the high performance of PM. In this paper, we make the case for an in-Kernel key-value storage service optimized for PM, called InK. While providing the persistence of data at a high performance, InK considers the characteristics of PM to guarantee the crash consistency. To this end, InK indexes key-value pairs with B+ tree, which is more efficient on PM. We implemented InK based on the Linux kernel and evaluated its performance with Yahoo Cloud Service Benchmark (YCSB) and RocksDB. Evaluation results confirms that InK has advantages over LSM-tree-based key-value store systems in terms of throughput and tail latency.
Self-interstitials in 3C-SiC

We report results from density-functional plane-wave pseudopotential calculations for carbon and silicon self-interstitials in cubic silicon carbide (3C-SiC). Several initial ionic configurations are used in the search for the global total-energy minimum including tetragonal, split [100] and split [110] geometries. Neutral carbon interstitials are found to have several nearly degenerate total-energy minima configurations in split-interstitial geometries, with formation energies ranging? besides higher metastable ones? from 6.3 to 6.7? eV in stoichiometric SiC. By contrast, the neutral silicon interstitials have a clear single minimum total-energy configuration at the tetrahedral configuration with carbon nearest neighbours, exhibiting a formation energy of 6.0? eV. The split interstitial in the [110] direction at the silicon site and the tetrahedral configuration with silicon nearest neighbours are metastable and have significantly higher formation energies. The present calculations indicate that the carbon interstitial introduces deep levels in the band gap while the silicon interstitial at the tetrahedral site behaves like a shallow donor.
Rapid Reduction of Herbicide Susceptibility in Junglerice by Recurrent Selection with Sublethal Dose of Herbicides and Heat Stress

Global climate change, specifically rising temperature, can alter the molecular physiology of weedy plants. These changes affect herbicide efficacy and weed management. This research aimed to investigate the combined effect of heat stress (HS) and sublethal doses of herbicides (four active ingredients) on adaptive gene expression and efficacy of herbicide on Echinochloa colona (L.) Link (junglerice). Three factors were evaluated; factor A was E. colona generation (G0-original population from susceptible standard; G1 and G2 were progenies of recurrent selection), factor B was herbicide treatment (florpyrauxifen-benzyl, glufosinate-ammonium, imazethapyr, quinclorac and nontreated check) and factor C was HS (30 and 45 °C). The herbicides were applied at 0.125× the recommended dose. Recurrent exposure to HS, combined with sublethal doses of herbicides, favors the selection of plants less susceptible to the herbicide. Upregulation of defense (antioxidant) genes (APX: Ascorbate peroxidase), herbicide detoxification genes (CYP450 family: Cytochrome P450), stress acclimation genes (HSP: Heat shock protein, TPP: Trehalose phosphate phosphatase and TPS: Trehalose phosphate synthase) and genes related to herbicide conjugation (UGT: UDP Glucosyltransferase) was significant. The positive regulation of these genes may promote increased tolerance of E. colona to these herbicides.